Covid19Japan.com

Covid19 Japanが独自に収集している陽性者単位のデータ（個票データ）。ソースとデータは全てGitHubにて公開されており、データはJSON形式。「レコード数 \(\neq\) 累計陽性者数」であることに注意。

　

Import

Covid19 JapanがGitHubで公開しているデータは前述のようにJSON形式であり、最新データはlatest.jsonファイルで示されている。このため、読み込む際はひと工夫必要。

個票データ（Patient Data）

陽性者単位の個票データ。

path <- "https://raw.githubusercontent.com/reustle/covid19japan-data/master/docs/patient_data/"

df <- path %>% 
  paste0("latest.json") %>% 
  readr::read_lines() %>% 
  paste0(path, .) %>% 
  jsonlite::fromJSON()

df

　

集計データ（Summary Data）

死亡者数や重症者数などの推移データはsummaryフォルダ内のJSON形式ファイルにまとめられている。読み込むと分かるがリスト型で、その中データフレームが含まれる形式である。
summaryフォルダの他にsummary_minフォルダというフォルダがあるが、summary_minフォルダ内のJSONファイルは単に改行を省略して小さくしたファイル。

path <- "https://raw.githubusercontent.com/reustle/covid19japan-data/master/docs/summary/"

df_s <- path %>% 
  paste0("latest.json") %>% 
  readr::read_lines() %>% 
  paste0(path, .) %>% 
  jsonlite::fromJSON()

df_s %>% summary()

##             Length Class      Mode     
## prefectures 27     data.frame list     
## regions     12     data.frame list     
## daily       37     data.frame list     
## updated      1     -none-     character

　
三つのデータフレームと一つのベクトル（更新日時）から構成されている。データフレームは上から順に都道府県別、地方別、日次となっているが、Lengthを見てわかるようにそれぞれに含まれる集計データが異なっている。

　

都道府県単位集計

更新日時（$updated）における都道府県単位での累積値。厚生労働省がオープンデータから除いている空港検疫・ダイヤモンドプリンセス・長崎クルーズ船・その他が含まれるので全51区分になっている。

df_s$prefectures

陽性者・死亡者などの時系列集計データがネストされて格納されている。日付はネストされていないので、各項目に対するstartDateの項を参照すること。

項目	内容	備考
dailyConfirmedCount	陽性者数	単日
dailyConfirmedStartDate	陽性者数のカウント開始日	区分により開始日が異なる
dailyDeceasedCount	死亡者数	単日
dailyDeceasedStartDate	死亡者数のカウント開始日	区分により開始日が異なる
dailyRecoveredCumulative	快復者数	累計
dailyRecoveredStartDate	快復者数のカウント開始日	区分により開始日が異なる
dailyActive	治療者数1	単日
dailyActiveStartDate	治療者数のカウント開始日	区分により開始日が異なる

¹ 陽性者数から死亡者数と快復者数を引いた数値を治療者数としている

　

地方単位集計

更新日次時点における地方区分単位での累積値。陽性者の時系列集計データが都道府県単位データと同様にネストで格納されているが、死亡者・快復者・治療者のデータは含まれていない。
なお、時系列データの合計値と累積項の値が一致しない場合がある。

df_s$regions

df_s$regions$dailyConfirmedCount[[1]] %>% sum()

## [1] 61519

　

日次集計

個票データを日次で集計したもの。日付を見れば分かる通り暗黙の欠落を含んでいる。

df_s$daily

　

更新日時

集計データの更新日時。

df_s$updated

## [1] "2020-11-09T18:51:38+09:00"

　

Area Data

地域・地方ごとの分析を行う場合に便利な都道府県データを用意した。このデータはGistで公開している。

　

Others

その他のオープンデータ。

• 新型コロナウイルス対策病床オープンデータ

　

Data Wrangling

Summarize

最初に個票データの内容を確認する。これには要約に便利なskimrパッケージを用いる。

df %>% 
  skimr::skim()

Data summary

Name	Piped data
Number of rows	110864
Number of columns	23
_______________________
Column type frequency:
character	19
logical	3
numeric	1
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
patientId	0	1.00	1	8	0	109160	0
dateAnnounced	0	1.00	10	10	0	286	0
gender	18228	0.84	1	1	0	2	0
detectedPrefecture	0	1.00	3	15	0	49	0
patientStatus	106864	0.04	8	23	0	8	0
notes	57670	0.48	1	270	0	50422	1
mhlwPatientNumber	110415	0.00	1	11	0	434	0
prefecturePatientNumber	15900	0.86	5	20	0	94955	0
prefectureSourceURL	79560	0.28	5	224	0	3440	0
residence	26150	0.76	1	38	0	1423	0
sourceURL	637	0.99	1	239	0	8215	0
relatedPatients	100246	0.10	2	259	0	6442	0
knownCluster	108381	0.02	3	88	0	229	0
detectedCityTown	84570	0.24	2	22	0	663	0
cityPrefectureNumber	84838	0.23	1	34	0	26017	2
citySourceURL	98997	0.11	9	317	0	3646	0
deceasedDate	109023	0.02	10	10	0	236	0
deceasedReportedDate	109649	0.01	10	62	0	205	0
deathSourceURL	109794	0.01	14	123	0	651	0

Variable type: logical

skim_variable	n_missing	complete_rate	mean	count
confirmedPatient	0	1	0.98	TRU: 109159, FAL: 1705
charterFlightPassenger	110850	0	1.00	TRU: 14
cruisePassengerDisembarked	110853	0	1.00	TRU: 11

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
ageBracket	0	1	31.55	23.91	-1	20	30	50	100	▅▇▅▂▁

　
元がJSON形式なので、読み込んだ直後は殆どの変量（フィーチャー）が文字型になっていることが分かる。また、意外と欠損が多いことも分かる。

　

Tidy & Transform

各変量（フィーチャー）を適切な形式に変換し、地域区分でも分析できるように都道府県データと結合することで、ベースとなるデータセットを作成する。

x <- df %>% 
  dplyr::select(patientId, date = dateAnnounced, gender,
                pref = detectedPrefecture, patientStatus, knownCluster,
                confirmedPatient, charterFlightPassenger,
                cruisePassengerDisembarked, ageBracket,
                deceasedDate, deceasedReportedDate) %>% 
  dplyr::filter(confirmedPatient == TRUE) %>% 
  dplyr::mutate(date = lubridate::as_date(date),
                gender = forcats::as_factor(gender),
                patientStatus = forcats::as_factor(patientStatus),
                cluster = dplyr::if_else(!is.na(knownCluster), TRUE, FALSE),
                ageBracket = forcats::as_factor(ageBracket),
                deceasedDate = lubridate::as_date(deceasedDate),
                deceasedReportedDate = lubridate::as_date(deceasedReportedDate)) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  dplyr::select(-`推計人口`) %>% 
  dplyr::rename(Pref = `都道府県`, region = `八地方区分`)

x

変換結果を要約してみると

x %>% 
  skimr::skim()

Data summary

Name	Piped data
Number of rows	109159
Number of columns	19
_______________________
Column type frequency:
character	3
Date	3
factor	9
logical	4
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
patientId	0	1.00	1	8	0	109159	0
pref	0	1.00	3	15	0	49	0
knownCluster	106705	0.02	3	88	0	227	0

Variable type: Date

skim_variable	n_missing	complete_rate	min	max	median	n_unique
date	0	1	2020-01-15	2020-11-09	2020-08-15	286
deceasedDate	108780	0	2020-02-13	2020-10-17	2020-05-08	150
deceasedReportedDate	108829	0	2020-02-13	2020-10-17	2020-05-16	131

Variable type: factor

skim_variable	n_missing	complete_rate	ordered	n_unique	top_counts
gender	17546	0.84	FALSE	2	M: 51450, F: 40163
patientStatus	106626	0.02	FALSE	8	Hos: 1261, Dec: 371, Hom: 315, Dis: 283
ageBracket	0	1.00	FALSE	13	20: 25114, -1: 17643, 30: 15913, 40: 13206
pcode	1287	0.99	FALSE	47	13: 32805, 27: 13894, 14: 9390, 23: 6876
Pref	1287	0.99	FALSE	47	東京都: 32805, 大阪府: 13894, 神奈川: 9390, 愛知県: 6876
region	1287	0.99	FALSE	8	関東地: 56132, 近畿地: 21856, 九州地: 11259, 中部地: 10705
広域圏	9036	0.92	FALSE	8	首都圏: 56373, 近畿圏: 21260, 中部圏: 9353, 九州圏: 7720
通俗的区分	1287	0.99	FALSE	11	関東: 56132, 関西: 21260, 東海: 8970, 九州: 7720
fct_pref	1287	0.99	FALSE	47	Tok: 32805, Osa: 13894, Kan: 9390, Aic: 6876

Variable type: logical

skim_variable	n_missing	complete_rate	mean	count
confirmedPatient	0	1	1.00	TRU: 109159
charterFlightPassenger	109145	0	1.00	TRU: 14
cruisePassengerDisembarked	109148	0	1.00	TRU: 11
cluster	0	1	0.02	FAL: 106705, TRU: 2454

　
文字型を因子型に変換するだけでも大まかな傾向が見えるようになる。例えば

• 年齢別で見ると20代、30代、年齢不明（恐らく非回答）、40代の順に多い
• 都道府県別では東京、大阪、神奈川、愛知の順と人口にほぼ比例
• 地方区分で見ると関東、近畿、九州、中部となっており九州地方が以外と多い

ことが読める。

patientStatusは以下の通りで、ほぼ更新されていないのと思われる。死者数などの推移を見る場合はサマリデータを使った方がいい。

x %>% 
  dplyr::group_by(patientStatus) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  dplyr::mutate(Japanese = c("回復", "入院中", "退院済", "死亡", "詳細不明",
                             "重症", "自宅療養", "ホテル療養", NA))

　

集計（陽性者）

　

地方別集計

地方別で陽性者数と人口千人あたりの比率を比較する。

region <- prefs %>% 
  dplyr::group_by(`八地方区分`) %>% 
  dplyr::summarise(population = sum(`推計人口`)) %>% 
  dplyr::rename(region = `八地方区分`)

r_by_region <- x %>% 
  dplyr::group_by(region) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  dplyr::left_join(region, by = c("region" = "region")) %>% 
  dplyr::select(region, n, population) %>% 
  dplyr::mutate(rate = round(n / population, 2))
r_by_region

　

都道府県別集計

同様に都道府県別に比較する。

r_by_prefs <- x %>% 
  dplyr::group_by(Pref) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  dplyr::left_join(prefs, by = c("Pref" = "都道府県")) %>% 
  dplyr::select(Pref, n, population = `推計人口`) %>% 
  dplyr::mutate(rate = round(n / population, 2))
r_by_prefs %>% 
  tibble::rowid_to_column("No") %>% 
  DT::datatable()

　

累計陽性者数トップ10

x %>% 
  dplyr::group_by(Pref) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::left_join(prefs, by = c("Pref" = "都道府県")) %>% 
  dplyr::select(Pref, n, population = `推計人口`) %>% 
  dplyr::mutate(rate = round(n / population, 2)) %>% 
  dplyr::slice_max(order_by = n, n = 10) %>% 
  dplyr::rename(`累計陽性者数` = n, `推計人口[千人]` = population, `率` = rate)

　

人口千人あたりの比率トップ10

x %>% 
  dplyr::group_by(Pref) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::left_join(prefs, by = c("Pref" = "都道府県")) %>% 
  dplyr::select(Pref, n, population = `推計人口`) %>% 
  dplyr::mutate(rate = round(n / population, 2)) %>% 
  dplyr::slice_max(order_by = rate, n = 10) %>% 
  dplyr::rename(`累計陽性者数` = n, `推計人口[千人]` = population, `率` = rate)

　

クラスタ比率

x %>% 
  dplyr::group_by(region, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
  dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1))

　

x %>% 
  dplyr::group_by(Pref, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
  dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1)) %>% 
  tidyr::replace_na(list(`TRUE` = 0L, ratio = 0.0)) %>% 
  tibble::rowid_to_column(var = "No") %>% 
  DT::datatable()

　

日次集計（陽性者）

　

全国日次集計

日次単位の陽性者数、前日差、累計、移動平均（7日）を求める。

x_by_all <- x %>% 
  dplyr::group_by(date) %>% 
  dplyr::filter(!is.na(Pref)) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day"),
                  fill = list(n = 0L)) %>% 
  dplyr::mutate(diff = lagdiff(n), cum = cumsum(n),
                ma7 = zoo::rollmeanr(n, k = 7L, na.pad = TRUE))

x_by_all

　

地方区分別日次集計

同様に地方区分別の集計を求める。

x_by_region <- x %>% 
  dplyr::filter(!is.na(Pref)) %>% 
  dplyr::group_by(date, region) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  tidyr::pivot_wider(names_from = region, values_from = n, values_fill = 0L) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day")) %>% 
  tidyr::pivot_longer(cols = -date, names_to = "region", values_to = "n") %>% 
  tidyr::replace_na(replace = list(n = 0L)) %>% 
  dplyr::group_by(region) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs %>% dplyr::distinct(`八地方区分`), .,
                   by = c("八地方区分" = "region")) %>% 
  dplyr::mutate(region = forcats::fct_inorder(`八地方区分`)) %>% 
  dplyr::select(date, region, n, diff, cum, ma7) %>% 
  dplyr::arrange(date)
x_by_region

　

都道府県別日次集計

同様に都道府県別の集計を求める。

x_by_prefs <- x %>% 
  dplyr::filter(!is.na(Pref)) %>% 
  dplyr::group_by(date, Pref) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  tidyr::pivot_wider(names_from = Pref, values_from = n, values_fill = 0L) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day")) %>% 
  tidyr::pivot_longer(cols = -date, names_to = "Pref", values_to = "n") %>% 
  tidyr::replace_na(replace = list(n = 0L)) %>% 
  dplyr::group_by(Pref) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs, ., by = c("都道府県" = "Pref")) %>% 
  dplyr::mutate(Pref = forcats::fct_inorder(`都道府県`)) %>% 
  dplyr::select(date, Pref, n, diff, cum, ma7) %>% 
  dplyr::arrange(date)
x_by_prefs

　

日次集計（死亡者）

　

都道府県別

都道府県別の日次単位の死亡者数、前日差、累計、移動平均（7日）を求める。

start <- df_s$prefectures %>% 
  dplyr::select(pref = name, date = dailyDeceasedStartDate) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  dplyr::arrange(pcode) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::select(date, Pref = `都道府県`) %>% 
  dplyr::distinct(date) %>% 
  .$date %>% lubridate::as_date()

d_by_prefs <- df_s$prefectures %>% 
  dplyr::select(deceased = dailyDeceasedCount, pref = name) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::select(Pref = `都道府県`, deceased) %>% 
  tidyr::unnest(deceased) %>% 
  tidyr::pivot_wider(names_from = Pref, values_from = deceased) %>% 
  tidyr::unnest() %>% 
  dplyr::mutate(date = seq.Date(from = start, to = start + nrow(.) - 1,
                                by = "day")) %>% 
  dplyr::select(date, dplyr::everything()) %>% 
  tidyr::pivot_longer(col = -date, names_to = "Pref", values_to = "n") %>% 
  dplyr::group_by(Pref) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs, ., by = c("都道府県" = "Pref")) %>% 
  dplyr::mutate(Pref = forcats::fct_inorder(`都道府県`)) %>% 
  dplyr::select(date, Pref, n, diff, cum, ma7) %>% 
  dplyr::arrange(date)
d_by_prefs

　

地方別

集計データ$regionsには死亡者数の日次データが存在しないため$prefecturesのデータから計算する。

d_by_region <- d_by_prefs %>% 
  dplyr::select(date, pref = Pref, n) %>% 
  dplyr::left_join(prefs, by = c("pref" = "都道府県")) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::group_by(date, `八地方区分`) %>% 
  dplyr::summarise(n = sum(n)) %>% 
  dplyr::ungroup() %>% 
  dplyr::rename(region = `八地方区分`) %>% 
  dplyr::group_by(region) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::arrange(date)
d_by_region

　

陽性者比率と死亡者比率

rpd_by_all <- d_by_region %>% 
  dplyr::group_by(region) %>% 
  dplyr::summarise(d = sum(n)) %>% 
  dplyr::left_join(r_by_region, ., by = c("region")) %>% 
  dplyr::select(region, positive = n, deceased = d, population) %>% 
  dplyr::select(-region) %>% 
  dplyr::summarise_all(sum) %>% 
  dplyr::mutate(p_rate = round(positive / population, 2),
                d_rate = round(deceased / positive, 2))

rpd_by_all %>% 
  dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
                `推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
                `陽性者に対する死亡者比率` = d_rate)

　

rpd_by_region <- d_by_region %>% 
  dplyr::group_by(region) %>% 
  dplyr::summarise(d = sum(n)) %>% 
  dplyr::left_join(r_by_region, ., by = c("region")) %>% 
  dplyr::select(region, positive = n, deceased = d, population, p_rate = rate) %>% 
  dplyr::mutate(d_rate = round(deceased / positive, 2))

rpd_by_region %>% 
  dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
                `推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
                `陽性者に対する死亡者比率` = d_rate)

　

rpd_by_prefs <- d_by_prefs %>% 
  dplyr::group_by(Pref) %>% 
  dplyr::summarise(d = sum(n)) %>% 
  dplyr::left_join(r_by_prefs, ., by = "Pref") %>% 
  dplyr::select(Pref, positive = n, deceased = d, population, p_rate = rate) %>% 
  dplyr::mutate(d_rate = round(deceased / positive, 2)) 

rpd_by_prefs %>% 
  dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
                `推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
                `陽性者に対する死亡者比率` = d_rate)

　

全国日次集計

都道府県別のデータから全国の日次集計を求める。

d_by_all <- d_by_prefs %>% 
  dplyr::group_by(date) %>% 
  dplyr::summarise(n = sum(n)) %>% 
  dplyr::ungroup() %>% 
  dplyr::mutate(diff = lagdiff(n), cum = cumsum(n), ma7 = ma7(n))
d_by_all

　

Visualize

　

陽性者の日次推移

　

全国

sec_scale <- 100
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

x_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n), stat = "identity", width = 1.0,
                      alpha = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7), linetype = "dashed",
                       colour = "dark green", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale),
                       colour = "dark green", size = 1.0) +
    ggplot2::labs(title = paste0("全国の陽性者数推移（単日） @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") +
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

x_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff), colour = "dark green", alpha = 0.5) + 
    ggplot2::labs(title = paste0("全国の陽性者数前日差 @", datetime),
                  subtitle = subtitle, caption = caption, 
                  x = "", y = "前日差")

　

地方別

陽性者数（単日）

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = n)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      width = 1.0, alpha = 0.5) + 
    ggplot2::labs(title = paste0("地方別陽性者数推移（単日） @", datetime),
                  caption = caption, 
                  x = "", y = "陽性者数") 

　

移動平均

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = ma7, colour = region)) + 
    ggplot2::geom_line(size = 1) +
    ggplot2::theme(legend.position = 'none') +
    ggplot2::labs(title = paste0("地方別7日間移動平均 @", datetime),
                  caption = caption,
                  x = "", y = "陽性者数") + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region),
                             data = subset(x_by_region, date == max(date)),
                             nudge_x = 30, segment.alpha = 0.5, size = 3) + 
    ggplot2::lims(x = c(min(x_by_region$date),
                        max(x_by_region$date) + 45))

　

累積

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = cum, colour = region)) + 
    ggplot2::geom_line() +
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("地方別累積陽性者数 @", datetime),
                  caption = caption,
                  x = "", y = "累積陽性者数") + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region),
                             data = subset(x_by_region, date == max(date)))

　

単日＋累積

sec_scale <- 50
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      alpha = 0.5, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = region)) +
    ggplot2::facet_wrap(~ region) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      alpha = 0.5, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = region),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = region)) +
    ggplot2::facet_wrap(~ region, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

前日差

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff, colour = region)) +
    ggplot2::facet_wrap(~ region, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数前日差, Free Y scale @", datetime),
                  caption = caption, x = "", y = "")

　

都道府県別

　

単日＋累計

sec_scale <- 100
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


x_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = Pref), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = Pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = Pref)) +
    ggplot2::facet_wrap(~ Pref, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

x_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
  ggplot2::geom_bar(ggplot2::aes(y = n, fill = Pref), stat = "identity",
                      alpha = 0.35, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = Pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = Pref)) +
    ggplot2::facet_wrap(~ Pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

　

前日差

x_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff, colour = Pref)) +
    ggplot2::facet_wrap(~ Pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数前日差, Free Y scale @", datetime),
                  x = "", y = "")

　

死亡者の日次推移

　

全国

sec_scale <- 100
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

d_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n), stat = "identity", width = 1.0,
                      alpha = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7), linetype = "dashed",
                       colour = "dark green", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale),
                       colour = "dark green", size = 1.0) +
    ggplot2::labs(title = paste0("全国の死亡者数推移（単日） @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") +
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff), colour = "dark green", alpha = 0.5) + 
    ggplot2::labs(title = paste0("全国の死亡者数前日差 @", datetime),
                  subtitle = subtitle, caption = caption, 
                  x = "", y = "前日差")

　

地方別

sec_scale <- 50
ncol <- 4
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


d_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = region),
                       linetype = "solid", size = 0.2) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = region)) +
    ggplot2::facet_wrap(~ region, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = region),
                       linetype = "solid", size = 0.2) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = region)) +
    ggplot2::facet_wrap(~ region, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

　

都道府県別日次推移

sec_scale <- 10
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


d_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = Pref), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = Pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = Pref)) +
    ggplot2::facet_wrap(~ Pref, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数（単日）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
  ggplot2::geom_bar(ggplot2::aes(y = n, fill = Pref), stat = "identity",
                      alpha = 0.35, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = Pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = Pref)) +
    ggplot2::facet_wrap(~ Pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数（単日）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

　

比較

陽性者数と死亡者の比較。

　

全国

sec_scale <- (1 / 50)

x_by_all %>% 
  dplyr::left_join(d_by_all, by = c("date")) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n.x), stat = "identity",
                      fill = "dark green", alpha = 0.25, width = 1.0) +
    ggplot2::geom_bar(ggplot2::aes(y = n.y / sec_scale), stat = "identity",
                      fill = "dark red", alpha = 0.25, width = 1.0) +
    # ggplot2::geom_line(ggplot2::aes(y = n.x), colour = "dark green") + 
    # ggplot2::geom_line(ggplot2::aes(y = n.y / sec_scale), colour = "dark red") + 
    ggplot2::labs(title = paste0("@", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数（濃緑）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "死亡者数（濃赤）")
    )

　

地方別

sec_scale <- (1 / 10)
ncol <- 4

x_by_region %>% 
  dplyr::left_join(d_by_region, by = c("date" = "date", "region" = "region")) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n.x), stat = "identity",
                      fill = "dark green", alpha = 0.25, width = 1.0) +
    ggplot2::geom_bar(ggplot2::aes(y = n.y / sec_scale), stat = "identity",
                      fill = "dark red", alpha = 0.25, width = 1.0) +
    # ggplot2::geom_line(ggplot2::aes(y = n.x), colour = "dark green") + 
    # ggplot2::geom_line(ggplot2::aes(y = n.y / sec_scale), colour = "dark red") + 
    ggplot2::facet_wrap(~ region, ncol = ncol, scales = "free_y") + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数（濃緑）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "死亡者数（濃赤）")
    )

　

相関

　

地方区分別

r_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = region)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region, colour = region)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

rpd_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) + 
    ggplot2::geom_point(ggplot2::aes(colour = region)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region, colour = region)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "陽性者数", y = "死亡者数")

　

都道府県別

r_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = Pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = Pref, colour = Pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("@", datetime), caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

r_by_prefs %>% 
  dplyr::filter(n < 5000) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = Pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = Pref, colour = Pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("累計陽性者数五千人未満 @", datetime),
                  caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

rpd_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) + 
    ggplot2::geom_abline(slope = rpd_by_all$d_rate, intercept = 0, colour = "gray") +
    ggplot2::geom_point(ggplot2::aes(colour = Pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = Pref, colour = Pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "陽性者数", y = "死亡者数")

　

rpd_by_prefs %>% 
  dplyr::filter(positive < 1000) %>% 
  ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) + 
    ggplot2::geom_abline(slope = rpd_by_all$d_rate, intercept = 0, colour = "gray") +
    ggplot2::geom_point(ggplot2::aes(colour = Pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = Pref, colour = Pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "陽性者数", y = "死亡者数")

　

Model

時系列（TS）分析

日本の時系列データは週単位の変動が認められるので、frequencyを7に設定して陽性者数のデータをtsオブジェクトに変換する。

ts_week <- x_by_all %>% 
  dplyr::select(n) %>% 
  ts(frequency = 7)

時系列データに変換したものをプロットすると可視化の項でプロットした棒グラフと同じような形のグラフになることが分かります。

ts_week %>% 
  plot(main = paste0("全国 @", datetime))

上記からトレンド（長期的傾向）を除いたグラフ。デフォルト指定なのでlag = 1。つまり、前日差。

ts_week %>% 
    base::diff() %>% 
  plot(main = paste0("全国 @", datetime))

トレンド、季節変動（周期変動）、非周期変動に分解した場合。frequency = 1では分解できない点に注意。

ts_week %>% 
  stats::decompose() %>% 
  plot()

トレンドを抜き出してみる。移動平均に酷似している。

ts_week %>% 
  stats::decompose() %>% 
  .$x %>% 
  plot(ylim = c(0, 1500), main = paste0("全国 @", datetime))

par(new = TRUE)

ts_week %>% 
  stats::decompose() %>% 
  .$trend %>% 
  plot(ylim = c(0, 1500), col = "dark green", lwd = 3)

　

地方別時系列分析

x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道地方
## NULL
## 
## $東北地方
## NULL
## 
## $関東地方
## NULL
## 
## $中部地方
## NULL
## 
## $近畿地方
## NULL
## 
## $中国地方
## NULL
## 
## $四国地方
## NULL
## 
## $九州地方
## NULL

oldpar <- par()
par(mfrow=c(4, 2))
x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name, ylim = c(0, max(.x)))
                # plot(.x, main = region)
                par(new = TRUE)
                stats::decompose(.x) %>% 
                  .$trend %>% 
                  plot(ylim = c(0, max(.x)), col = "dark green", lwd = 3)
                  # plot(col = "dark green", lwd = 2)
              } )

## $北海道地方
## NULL
## 
## $東北地方
## NULL
## 
## $関東地方
## NULL
## 
## $中部地方
## NULL
## 
## $近畿地方
## NULL
## 
## $中国地方
## NULL
## 
## $四国地方
## NULL
## 
## $九州地方
## NULL

par(oldpar)

x_by_prefs %>% 
  dplyr::select(Pref, n) %>% 
  split(.$Pref) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name, ylim = c(0, max(.x)))
                # plot(.x, main = region)
                par(new = TRUE)
                stats::decompose(.x) %>% 
                  .$trend %>% 
                  plot(ylim = c(0, max(.x)), col = "dark green", lwd = 3)
                  # plot(col = "dark green", lwd = 2)
              } )

## $北海道
## NULL
## 
## $青森県
## NULL
## 
## $岩手県
## NULL
## 
## $宮城県
## NULL
## 
## $秋田県
## NULL
## 
## $山形県
## NULL
## 
## $福島県
## NULL
## 
## $茨城県
## NULL
## 
## $栃木県
## NULL
## 
## $群馬県
## NULL
## 
## $埼玉県
## NULL
## 
## $千葉県
## NULL
## 
## $東京都
## NULL
## 
## $神奈川県
## NULL
## 
## $新潟県
## NULL
## 
## $富山県
## NULL
## 
## $石川県
## NULL
## 
## $福井県
## NULL
## 
## $山梨県
## NULL
## 
## $長野県
## NULL
## 
## $岐阜県
## NULL
## 
## $静岡県
## NULL
## 
## $愛知県
## NULL
## 
## $三重県
## NULL
## 
## $滋賀県
## NULL
## 
## $京都府
## NULL
## 
## $大阪府
## NULL
## 
## $兵庫県
## NULL
## 
## $奈良県
## NULL
## 
## $和歌山県
## NULL
## 
## $鳥取県
## NULL
## 
## $島根県
## NULL
## 
## $岡山県
## NULL
## 
## $広島県
## NULL
## 
## $山口県
## NULL
## 
## $徳島県
## NULL
## 
## $香川県
## NULL
## 
## $愛媛県
## NULL
## 
## $高知県
## NULL
## 
## $福岡県
## NULL
## 
## $佐賀県
## NULL
## 
## $長崎県
## NULL
## 
## $熊本県
## NULL
## 
## $大分県
## NULL
## 
## $宮崎県
## NULL
## 
## $鹿児島県
## NULL
## 
## $沖縄県
## NULL

　

Infer

時系列予測（ARIMA）

ARIMA（Auto Regressive Integrated Moving Average, 自己回帰和分移動平均）モデルによる陽性者に対する予測。予測に必要なパラメータはステップワイズにより自動的に最適なものが選択される。ただし、モデル自体を評価していないので、こういうことが出来る程度の話。

　

全国

x_by_all %>% 
  dplyr::select(n) %>% 
  ts(.$n, frequency = 7) %>% 
  forecast::auto.arima() %>%  
  forecast::forecast() %>% 
  plot(main = paste0("全国 @", datetime))

　

地方別

x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map(., forecast::auto.arima) %>% 
  purrr::map(., forecast::forecast) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道地方
## $北海道地方$mean
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##  [1] 178.7640 201.9842 214.9907 226.3208 253.7229 253.2695 275.5047 277.6097
##  [9] 290.3747 305.9110 315.7023 336.4917 342.2320 358.4719
## 
## $北海道地方$lower
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##               80%      95%
## 43.85714 169.1697 164.0909
## 44.00000 191.3920 185.7847
## 44.14286 201.4110 194.2224
## 44.28571 211.1090 203.0563
## 44.42857 236.1904 226.9092
## 44.57143 233.8261 223.5333
## 44.71429 253.8898 242.4475
## 44.85714 252.5891 239.3440
## 45.00000 262.7339 248.1018
## 45.14286 275.2018 258.9453
## 45.28571 282.1684 264.4166
## 45.42857 299.9497 280.6055
## 45.57143 302.7416 281.8366
## 45.71429 315.9400 293.4249
## 
## $北海道地方$upper
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##               80%      95%
## 43.85714 188.3582 193.4371
## 44.00000 212.5765 218.1837
## 44.14286 228.5704 235.7591
## 44.28571 241.5327 249.5854
## 44.42857 271.2554 280.5365
## 44.57143 272.7130 283.0058
## 44.71429 297.1196 308.5618
## 44.85714 302.6302 315.8754
## 45.00000 318.0154 332.6475
## 45.14286 336.6203 352.8768
## 45.28571 349.2363 366.9881
## 45.42857 373.0336 392.3778
## 45.57143 381.7225 402.6274
## 45.71429 401.0038 423.5189
## 
## 
## $東北地方
## $東北地方$mean
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##  [1] 16.49025 20.63184 23.50804 21.30327 19.50106 20.87880 17.00179 20.41981
##  [9] 20.43404 19.09310 21.13443 20.95352 21.30298 22.70699
## 
## $東北地方$lower
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##                80%       95%
## 43.85714  9.906838  6.421791
## 44.00000 13.852354 10.263510
## 44.14286 16.537994 12.848273
## 44.28571 14.147730 10.359816
## 44.42857 12.164714  8.281090
## 44.57143 13.366011  9.388979
## 44.71429  9.316598  5.248302
## 44.85714 12.363249  8.098364
## 45.00000 12.175527  7.803734
## 45.14286 10.637453  6.161305
## 45.28571 12.486151  7.908027
## 45.42857 12.116796  7.438917
## 45.57143 12.281748  7.506198
## 45.71429 13.504951  8.633689
## 
## $東北地方$upper
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##               80%      95%
## 43.85714 23.07366 26.55871
## 44.00000 27.41133 31.00017
## 44.14286 30.47809 34.16782
## 44.28571 28.45881 32.24672
## 44.42857 26.83740 30.72102
## 44.57143 28.39160 32.36863
## 44.71429 24.68699 28.75528
## 44.85714 28.47637 32.74126
## 45.00000 28.69255 33.06435
## 45.14286 27.54874 32.02489
## 45.28571 29.78272 34.36084
## 45.42857 29.79024 34.46812
## 45.57143 30.32420 35.09975
## 45.71429 31.90902 36.78028
## 
## 
## $関東地方
## $関東地方$mean
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##  [1] 398.7648 422.8115 532.5142 550.3329 562.2597 412.9522 295.9652 429.0657
##  [9] 423.8559 539.4957 536.3964 562.8186 407.0472 306.2060
## 
## $関東地方$lower
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##               80%       95%
## 43.85714 336.5668 303.64113
## 44.00000 345.3844 304.39691
## 44.14286 447.5619 402.59092
## 44.28571 462.9899 416.75328
## 44.42857 472.2941 424.66915
## 44.57143 320.0382 270.85244
## 44.71429 197.5494 145.45124
## 44.85714 319.3985 261.34417
## 45.00000 304.9579 242.01717
## 45.14286 414.9001 348.94325
## 45.28571 408.0888 340.16685
## 45.42857 431.8471 362.51498
## 45.57143 273.2620 202.44039
## 45.71429 169.2484  96.74739
## 
## $関東地方$upper
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##               80%      95%
## 43.85714 460.9629 493.8885
## 44.00000 500.2386 541.2260
## 44.14286 617.4665 662.4375
## 44.28571 637.6759 683.9125
## 44.42857 652.2253 699.8502
## 44.57143 505.8663 555.0520
## 44.71429 394.3810 446.4792
## 44.85714 538.7329 596.7872
## 45.00000 542.7538 605.6945
## 45.14286 664.0912 730.0481
## 45.28571 664.7040 732.6259
## 45.42857 693.7902 763.1223
## 45.57143 540.8325 611.6541
## 45.71429 443.1637 515.6647
## 
## 
## $中部地方
## $中部地方$mean
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##  [1] 131.4403 112.5437 124.7143 138.5412 158.6825 128.4481 121.5631 129.4151
##  [9] 130.6197 129.3385 142.9158 163.2713 133.9585 139.4819
## 
## $中部地方$lower
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##                80%       95%
## 43.85714 113.49894 104.00135
## 44.00000  90.33814  78.58321
## 44.14286  98.54450  84.69103
## 44.28571 108.29003  92.27603
## 44.42857 125.79100 108.37927
## 44.57143  92.32599  73.20412
## 44.71429  82.78980  62.26445
## 44.85714  85.18438  61.77005
## 45.00000  81.67897  55.77131
## 45.14286  76.74800  48.90828
## 45.28571  86.22986  56.22212
## 45.42857 103.11961  71.27725
## 45.57143  70.62782  37.10259
## 45.71429  72.67775  37.31374
## 
## $中部地方$upper
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##               80%      95%
## 43.85714 149.3817 158.8793
## 44.00000 134.7493 146.5042
## 44.14286 150.8842 164.7376
## 44.28571 168.7924 184.8064
## 44.42857 191.5741 208.9858
## 44.57143 164.5702 183.6920
## 44.71429 160.3364 180.8618
## 44.85714 173.6458 197.0601
## 45.00000 179.5604 205.4681
## 45.14286 181.9290 209.7687
## 45.28571 199.6018 229.6095
## 45.42857 223.4229 255.2653
## 45.57143 197.2892 230.8144
## 45.71429 206.2861 241.6502
## 
## 
## $近畿地方
## $近畿地方$mean
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##  [1] 251.3120 194.5843 237.6422 272.4136 268.0395 217.0910 160.3859 249.3714
##  [9] 214.4257 239.4558 264.6613 259.3326 216.7705 166.5691
## 
## $近畿地方$lower
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##                80%       95%
## 43.85714 220.27033 203.83785
## 44.00000 158.87431 139.97057
## 44.14286 199.87262 179.87862
## 44.28571 231.60871 210.00789
## 44.42857 225.39546 202.82105
## 44.57143 172.24008 148.49743
## 44.71429 113.93565  89.34638
## 44.85714 194.14168 164.90484
## 45.00000 155.21488 123.87056
## 45.14286 177.39985 144.54943
## 45.28571 199.59896 165.15702
## 45.42857 191.87572 156.16621
## 45.57143 146.92893 109.95705
## 45.71429  94.70518  56.66273
## 
## $近畿地方$upper
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##               80%      95%
## 43.85714 282.3537 298.7862
## 44.00000 230.2943 249.1981
## 44.14286 275.4118 295.4058
## 44.28571 313.2185 334.8194
## 44.42857 310.6836 333.2580
## 44.57143 261.9420 285.6846
## 44.71429 206.8361 231.4254
## 44.85714 304.6011 333.8379
## 45.00000 273.6365 304.9809
## 45.14286 301.5117 334.3621
## 45.28571 329.7237 364.1656
## 45.42857 326.7895 362.4990
## 45.57143 286.6120 323.5839
## 45.71429 238.4330 276.4754
## 
## 
## $中国地方
## $中国地方$mean
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##  [1]  9.502384 10.223182 10.223182 10.223182 10.223182 10.223182 10.223182
##  [8] 10.223182 10.223182 10.223182 10.223182 10.223182 10.223182 10.223182
## 
## $中国地方$lower
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##                  80%       95%
## 43.85714  0.47425188 -4.304953
## 44.00000  0.78411431 -4.212626
## 44.14286  0.60000410 -4.494199
## 44.28571  0.41935076 -4.770484
## 44.42857  0.24196659 -5.041770
## 44.57143  0.06768029 -5.308318
## 44.71429 -0.10366500 -5.570368
## 44.85714 -0.27221333 -5.828140
## 45.00000 -0.43809734 -6.081838
## 45.14286 -0.60143953 -6.331648
## 45.28571 -0.76235327 -6.577744
## 45.42857 -0.92094378 -6.820288
## 45.57143 -1.07730885 -7.059427
## 45.71429 -1.23153962 -7.295303
## 
## $中国地方$upper
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##               80%      95%
## 43.85714 18.53052 23.30972
## 44.00000 19.66225 24.65899
## 44.14286 19.84636 24.94056
## 44.28571 20.02701 25.21685
## 44.42857 20.20440 25.48813
## 44.57143 20.37868 25.75468
## 44.71429 20.55003 26.01673
## 44.85714 20.71858 26.27450
## 45.00000 20.88446 26.52820
## 45.14286 21.04780 26.77801
## 45.28571 21.20872 27.02411
## 45.42857 21.36731 27.26665
## 45.57143 21.52367 27.50579
## 45.71429 21.67790 27.74167
## 
## 
## $四国地方
## $四国地方$mean
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##  [1] 1.123725 1.216204 1.216204 1.216204 1.216204 1.216204 1.216204 1.216204
##  [9] 1.216204 1.216204 1.216204 1.216204 1.216204 1.216204
## 
## $四国地方$lower
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##                80%       95%
## 43.85714 -2.354472 -4.195717
## 44.00000 -2.517075 -4.493354
## 44.14286 -2.588005 -4.601831
## 44.28571 -2.657635 -4.708322
## 44.42857 -2.726036 -4.812932
## 44.57143 -2.793270 -4.915758
## 44.71429 -2.859395 -5.016887
## 44.85714 -2.924465 -5.116402
## 45.00000 -2.988527 -5.214377
## 45.14286 -3.051628 -5.310882
## 45.28571 -3.113810 -5.405980
## 45.42857 -3.175111 -5.499732
## 45.57143 -3.235568 -5.592193
## 45.71429 -3.295215 -5.683416
## 
## $四国地方$upper
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##               80%      95%
## 43.85714 4.601921 6.443167
## 44.00000 4.949484 6.925763
## 44.14286 5.020413 7.034239
## 44.28571 5.090043 7.140730
## 44.42857 5.158444 7.245340
## 44.57143 5.225678 7.348166
## 44.71429 5.291804 7.449296
## 44.85714 5.356873 7.548811
## 45.00000 5.420935 7.646786
## 45.14286 5.484036 7.743290
## 45.28571 5.546218 7.838389
## 45.42857 5.607519 7.932141
## 45.57143 5.667976 8.024602
## 45.71429 5.727623 8.115824
## 
## 
## $九州地方
## $九州地方$mean
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##  [1] 45.96751 48.68033 56.78302 53.78414 45.84161 47.09300 37.26368 46.20662
##  [9] 45.53245 50.56771 49.82133 45.31850 46.70642 41.76464
## 
## $九州地方$lower
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##                  80%        95%
## 43.85714  23.9513197  12.296655
## 44.00000  22.0256229   7.915475
## 44.14286  26.9276375  11.123155
## 44.28571  22.9815752   6.675681
## 44.42857  12.9959334  -4.391517
## 44.57143  10.0669941  -9.533388
## 44.71429  -3.5163590 -25.104006
## 44.85714  -0.7952546 -25.676542
## 45.00000  -6.1020045 -33.435634
## 45.14286  -5.1461648 -34.639309
## 45.28571  -9.1252148 -40.329633
## 45.42857 -17.0396523 -50.050063
## 45.57143 -19.4071961 -54.405631
## 45.71429 -27.9022987 -64.781746
## 
## $九州地方$upper
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##                80%       95%
## 43.85714  67.98369  79.63836
## 44.00000  75.33503  89.44518
## 44.14286  86.63839 102.44288
## 44.28571  84.58671 100.89261
## 44.42857  78.68729  96.07474
## 44.57143  84.11900 103.71938
## 44.71429  78.04371  99.63136
## 44.85714  93.20849 118.08978
## 45.00000  97.16691 124.50054
## 45.14286 106.28158 135.77473
## 45.28571 108.76788 139.97230
## 45.42857 107.67665 140.68706
## 45.57143 112.82004 147.81847
## 45.71429 111.43157 148.31102

　

都道府県別

x_by_prefs %>% 
  dplyr::select(Pref, n) %>% 
  split(.$Pref) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map(., forecast::auto.arima) %>% 
  purrr::map(., forecast::forecast) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道
## $北海道$mean
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##  [1] 178.7640 201.9842 214.9907 226.3208 253.7229 253.2695 275.5047 277.6097
##  [9] 290.3747 305.9110 315.7023 336.4917 342.2320 358.4719
## 
## $北海道$lower
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##               80%      95%
## 43.85714 169.1697 164.0909
## 44.00000 191.3920 185.7847
## 44.14286 201.4110 194.2224
## 44.28571 211.1090 203.0563
## 44.42857 236.1904 226.9092
## 44.57143 233.8261 223.5333
## 44.71429 253.8898 242.4475
## 44.85714 252.5891 239.3440
## 45.00000 262.7339 248.1018
## 45.14286 275.2018 258.9453
## 45.28571 282.1684 264.4166
## 45.42857 299.9497 280.6055
## 45.57143 302.7416 281.8366
## 45.71429 315.9400 293.4249
## 
## $北海道$upper
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##               80%      95%
## 43.85714 188.3582 193.4371
## 44.00000 212.5765 218.1837
## 44.14286 228.5704 235.7591
## 44.28571 241.5327 249.5854
## 44.42857 271.2554 280.5365
## 44.57143 272.7130 283.0058
## 44.71429 297.1196 308.5618
## 44.85714 302.6302 315.8754
## 45.00000 318.0154 332.6475
## 45.14286 336.6203 352.8768
## 45.28571 349.2363 366.9881
## 45.42857 373.0336 392.3778
## 45.57143 381.7225 402.6274
## 45.71429 401.0038 423.5189
## 
## 
## $青森県
## $青森県$mean
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##  [1] 0.9763212 3.6597959 1.9813302 3.0311807 2.3745178 2.7852488 2.5283439
##  [8] 2.6890333 2.5885249 2.6513912 2.6120695 2.6366645 2.6212807 2.6309030
## 
## $青森県$lower
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##                 80%        95%
## 43.85714 -1.5771849 -2.9289293
## 44.00000  0.6697326 -0.9131112
## 44.14286 -1.0772942 -2.6964322
## 44.28571 -0.2596417 -2.0016979
## 44.42857 -1.0262013 -2.8264332
## 44.57143 -0.7830376 -2.6719743
## 44.71429 -1.1619580 -3.1154859
## 44.85714 -1.1418786 -3.1698409
## 45.00000 -1.3642135 -3.4566668
## 45.14286 -1.4278324 -3.5872429
## 45.28571 -1.5847614 -3.8064295
## 45.42857 -1.6776434 -3.9615000
## 45.57143 -1.8055098 -4.1489111
## 45.71429 -1.9067273 -4.3088036
## 
## $青森県$upper
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##               80%      95%
## 43.85714 3.529827 4.881572
## 44.00000 6.649859 8.232703
## 44.14286 5.039955 6.659093
## 44.28571 6.322003 8.064059
## 44.42857 5.775237 7.575469
## 44.57143 6.353535 8.242472
## 44.71429 6.218646 8.172174
## 44.85714 6.519945 8.547908
## 45.00000 6.541263 8.633717
## 45.14286 6.730615 8.890025
## 45.28571 6.808900 9.030568
## 45.42857 6.950972 9.234829
## 45.57143 7.048071 9.391473
## 45.71429 7.168533 9.570610
## 
## 
## $岩手県
## $岩手県$mean
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##  [1] 0.67449577 0.17737778 0.14861524 0.50314774 0.17150710 0.17735205
##  [7] 0.17737876 0.06183877 0.22412158 0.22925615 0.12750097 0.22520763
## [13] 0.22412637 0.22412140
## 
## $岩手県$lower
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##                  80%        95%
## 43.85714  0.08898700 -0.2209626
## 44.00000 -0.40881411 -0.7191253
## 44.14286 -0.43825897 -0.7489314
## 44.28571 -0.08440801 -0.3954412
## 44.42857 -0.41672940 -0.7281230
## 44.57143 -0.41156441 -0.7233179
## 44.71429 -0.41221687 -0.7243299
## 44.85714 -0.55538080 -0.8821171
## 45.00000 -0.39413229 -0.7214161
## 45.14286 -0.39003029 -0.7178607
## 45.28571 -0.49281632 -0.8211925
## 45.42857 -0.39613880 -0.7250597
## 45.57143 -0.39824750 -0.7277123
## 45.71429 -0.39927823 -0.7292861
## 
## $岩手県$upper
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##                80%      95%
## 43.85714 1.2600045 1.569954
## 44.00000 0.7635697 1.073881
## 44.14286 0.7354895 1.046162
## 44.28571 1.0907035 1.401737
## 44.42857 0.7597436 1.071137
## 44.57143 0.7662685 1.078022
## 44.71429 0.7669744 1.079087
## 44.85714 0.6790583 1.005795
## 45.00000 0.8423754 1.169659
## 45.14286 0.8485426 1.176373
## 45.28571 0.7478183 1.076194
## 45.42857 0.8465541 1.175475
## 45.57143 0.8465002 1.175965
## 45.71429 0.8475210 1.177529
## 
## 
## $宮城県
## $宮城県$mean
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##  [1] 13.57648 12.07227 13.23398 12.33678 13.02969 12.49456 12.90784 12.58866
##  [9] 12.83517 12.64479 12.79182 12.67827 12.76596 12.69823
## 
## $宮城県$lower
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##               80%      95%
## 43.85714 9.076972 6.695076
## 44.00000 7.519639 5.109623
## 44.14286 8.264413 5.633684
## 44.28571 7.269718 4.587376
## 44.42857 7.664327 4.824076
## 44.57143 7.004029 4.097520
## 44.71429 7.181091 4.149532
## 44.85714 6.722583 3.617271
## 45.00000 6.768646 3.557225
## 45.14286 6.433403 3.145295
## 45.28571 6.402332 3.019942
## 45.42857 6.143327 2.683939
## 45.57143 6.067913 2.522180
## 45.71429 5.856780 2.235133
## 
## $宮城県$upper
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##               80%      95%
## 43.85714 18.07599 20.45789
## 44.00000 16.62489 19.03491
## 44.14286 18.20354 20.83427
## 44.28571 17.40385 20.08619
## 44.42857 18.39505 21.23530
## 44.57143 17.98508 20.89159
## 44.71429 18.63459 21.66615
## 44.85714 18.45473 21.56005
## 45.00000 18.90168 22.11311
## 45.14286 18.85617 22.14428
## 45.28571 19.18130 22.56369
## 45.42857 19.21320 22.67259
## 45.57143 19.46401 23.00974
## 45.71429 19.53969 23.16133
## 
## 
## $秋田県
## $秋田県$mean
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##  [1] 0.1963729 0.2231505 0.2231505 0.2231505 0.2231505 0.2231505 0.2231505
##  [8] 0.2231505 0.2231505 0.2231505 0.2231505 0.2231505 0.2231505 0.2231505
## 
## $秋田県$lower
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##                80%       95%
## 43.85714 -1.033113 -1.683963
## 44.00000 -1.017854 -1.674802
## 44.14286 -1.017854 -1.674802
## 44.28571 -1.017854 -1.674802
## 44.42857 -1.017854 -1.674802
## 44.57143 -1.017854 -1.674802
## 44.71429 -1.017854 -1.674802
## 44.85714 -1.017854 -1.674802
## 45.00000 -1.017854 -1.674802
## 45.14286 -1.017854 -1.674802
## 45.28571 -1.017854 -1.674802
## 45.42857 -1.017854 -1.674802
## 45.57143 -1.017854 -1.674802
## 45.71429 -1.017854 -1.674802
## 
## $秋田県$upper
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##               80%      95%
## 43.85714 1.425859 2.076709
## 44.00000 1.464155 2.121103
## 44.14286 1.464155 2.121103
## 44.28571 1.464155 2.121103
## 44.42857 1.464155 2.121103
## 44.57143 1.464155 2.121103
## 44.71429 1.464155 2.121103
## 44.85714 1.464155 2.121103
## 45.00000 1.464155 2.121103
## 45.14286 1.464155 2.121103
## 45.28571 1.464155 2.121103
## 45.42857 1.464155 2.121103
## 45.57143 1.464155 2.121103
## 45.71429 1.464155 2.121103
## 
## 
## $山形県
## $山形県$mean
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##  [1] 0.4092493 0.4943956 0.5638134 0.6455800 0.6969207 0.5270842 0.5355905
##  [8] 0.6923869 0.6797564 0.6623095 0.6410314 0.6168363 0.5905607 0.5629601
## 
## $山形県$lower
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##                 80%        95%
## 43.85714 -0.5182062 -1.0091716
## 44.00000 -0.4495107 -0.9491846
## 44.14286 -0.4053765 -0.9184346
## 44.28571 -0.3564096 -0.8868308
## 44.42857 -0.3432677 -0.8939102
## 44.57143 -0.5543699 -1.1268571
## 44.71429 -0.5880325 -1.1828426
## 44.85714 -0.4370677 -1.0349649
## 45.00000 -0.4809533 -1.0953959
## 45.14286 -0.5250355 -1.1535779
## 45.28571 -0.5685145 -1.2088095
## 45.42857 -0.6107960 -1.2606654
## 45.57143 -0.6514514 -1.3089329
## 45.71429 -0.6901836 -1.3535579
## 
## $山形県$upper
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##               80%      95%
## 43.85714 1.336705 1.827670
## 44.00000 1.438302 1.937976
## 44.14286 1.533003 2.046061
## 44.28571 1.647570 2.177991
## 44.42857 1.737109 2.287752
## 44.57143 1.608538 2.181025
## 44.71429 1.659214 2.254024
## 44.85714 1.821842 2.419739
## 45.00000 1.840466 2.454909
## 45.14286 1.849654 2.478197
## 45.28571 1.850577 2.490872
## 45.42857 1.844469 2.494338
## 45.57143 1.832573 2.490054
## 45.71429 1.816104 2.479478
## 
## 
## $福島県
## $福島県$mean
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##  [1] 4.562570 2.633884 3.753675 2.818003 3.064954 3.075984 3.271401 4.641598
##  [9] 3.146947 4.148258 3.352557 3.510931 3.436927 3.562018
## 
## $福島県$lower
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##                  80%         95%
## 43.85714  2.04438316  0.71133531
## 44.00000  0.04078940 -1.33191183
## 44.14286  1.08777765 -0.32346321
## 44.28571  0.08123869 -1.36751693
## 44.42857  0.25911166 -1.22621151
## 44.57143  0.20272470 -1.31828714
## 44.71429  0.33227023 -1.22361188
## 44.85714  1.54710074 -0.09102715
## 45.00000 -0.02675988 -1.70681885
## 45.14286  0.89727093 -0.82369778
## 45.28571  0.02608451 -1.73484378
## 45.42857  0.11064798 -1.68935302
## 45.57143 -0.03559754 -1.87384092
## 45.71429  0.01872429 -1.85698194
## 
## $福島県$upper
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##               80%      95%
## 43.85714 7.080758 8.413806
## 44.00000 5.226978 6.599679
## 44.14286 6.419573 7.830813
## 44.28571 5.554768 7.003523
## 44.42857 5.870796 7.356119
## 44.57143 5.949244 7.470256
## 44.71429 6.210533 7.766415
## 44.85714 7.736095 9.374223
## 45.00000 6.320654 8.000713
## 45.14286 7.399245 9.120214
## 45.28571 6.679030 8.439958
## 45.42857 6.911214 8.711215
## 45.57143 6.909451 8.747695
## 45.71429 7.105311 8.981017
## 
## 
## $茨城県
## $茨城県$mean
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##  [1] 7.683619 7.832666 8.428602 8.125056 8.457082 8.678130 8.165858 8.243423
##  [9] 8.243423 8.243423 8.243423 8.243423 8.243423 8.243423
## 
## $茨城県$lower
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##               80%       95%
## 43.85714 4.256822 2.4427852
## 44.00000 4.291026 2.4161946
## 44.14286 4.775727 2.8420123
## 44.28571 4.364236 2.3733777
## 44.42857 4.591329 2.5449234
## 44.57143 4.710219 2.6097332
## 44.71429 4.098353 1.9451459
## 44.85714 3.991412 1.7405336
## 45.00000 3.878930 1.5685072
## 45.14286 3.769275 1.4008042
## 45.28571 3.662244 1.2371142
## 45.42857 3.557657 1.0771623
## 45.57143 3.455354 0.9207033
## 45.71429 3.355191 0.7675181
## 
## $茨城県$upper
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##               80%      95%
## 43.85714 11.11042 12.92445
## 44.00000 11.37431 13.24914
## 44.14286 12.08148 14.01519
## 44.28571 11.88588 13.87673
## 44.42857 12.32283 14.36924
## 44.57143 12.64604 14.74653
## 44.71429 12.23336 14.38657
## 44.85714 12.49543 14.74631
## 45.00000 12.60792 14.91834
## 45.14286 12.71757 15.08604
## 45.28571 12.82460 15.24973
## 45.42857 12.92919 15.40968
## 45.57143 13.03149 15.56614
## 45.71429 13.13165 15.71933
## 
## 
## $栃木県
## $栃木県$mean
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##  [1] 1.297202 1.262178 1.319589 1.576758 1.614659 1.297339 1.471007 1.515839
##  [9] 1.563562 1.579205 1.592625 1.597786 1.601630 1.603279
## 
## $栃木県$lower
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##                80%       95%
## 43.85714 -1.642185 -3.198203
## 44.00000 -1.712855 -3.287743
## 44.14286 -1.809310 -3.465649
## 44.28571 -1.576763 -3.246136
## 44.42857 -1.573458 -3.261146
## 44.57143 -1.907887 -3.604631
## 44.71429 -1.751880 -3.457974
## 44.85714 -1.850749 -3.632913
## 45.00000 -1.827425 -3.622506
## 45.14286 -1.850463 -3.666020
## 45.28571 -1.858160 -3.684895
## 45.42857 -1.875647 -3.714371
## 45.57143 -1.890835 -3.739634
## 45.71429 -1.908197 -3.767060
## 
## $栃木県$upper
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##               80%      95%
## 43.85714 4.236590 5.792607
## 44.00000 4.237212 5.812100
## 44.14286 4.448488 6.104827
## 44.28571 4.730279 6.399653
## 44.42857 4.802776 6.490463
## 44.57143 4.502566 6.199310
## 44.71429 4.693894 6.399987
## 44.85714 4.882427 6.664591
## 45.00000 4.954550 6.749630
## 45.14286 5.008874 6.824430
## 45.28571 5.043410 6.870145
## 45.42857 5.071219 6.909943
## 45.57143 5.094095 6.942894
## 45.71429 5.114755 6.973618
## 
## 
## $群馬県
## $群馬県$mean
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##  [1] 4.029286 4.809195 3.817859 4.535633 4.857591 4.741337 4.635423 5.078471
##  [9] 5.131297 5.233633 5.205649 5.368793 5.364511 5.446153
## 
## $群馬県$lower
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##                 80%       95%
## 43.85714 -0.4614077 -2.838637
## 44.00000 -0.3346339 -3.057613
## 44.14286 -1.5289218 -4.359337
## 44.28571 -0.8750383 -3.739275
## 44.42857 -0.8752187 -3.909985
## 44.57143 -1.0904811 -4.177659
## 44.71429 -1.3088345 -4.455534
## 44.85714 -0.9729871 -4.176436
## 45.00000 -1.0615771 -4.339887
## 45.14286 -1.0107461 -4.316321
## 45.28571 -1.1048155 -4.445373
## 45.42857 -0.9844577 -4.347665
## 45.57143 -1.0443827 -4.437046
## 45.71429 -0.9934755 -4.402409
## 
## $群馬県$upper
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##                80%      95%
## 43.85714  8.519980 10.89721
## 44.00000  9.953025 12.67600
## 44.14286  9.164640 11.99505
## 44.28571  9.946305 12.81054
## 44.42857 10.590401 13.62517
## 44.57143 10.573156 13.66033
## 44.71429 10.579681 13.72638
## 44.85714 11.129930 14.33338
## 45.00000 11.324172 14.60248
## 45.14286 11.478012 14.78359
## 45.28571 11.516113 14.85667
## 45.42857 11.722043 15.08525
## 45.57143 11.773405 15.16607
## 45.71429 11.885781 15.29471
## 
## 
## $埼玉県
## $埼玉県$mean
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##  [1] 52.73190 57.35856 53.96689 87.71880 68.42416 61.25884 60.14804 62.40132
##  [9] 63.86001 62.30565 80.15936 71.33809 66.22638 67.48961
## 
## $埼玉県$lower
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##               80%      95%
## 43.85714 39.53732 32.55253
## 44.00000 43.63249 36.36635
## 44.14286 39.72916 32.19216
## 44.28571 72.98718 65.18872
## 44.42857 53.21467 45.16325
## 44.57143 45.58604 37.28936
## 44.71429 44.02524 35.49035
## 44.85714 44.01410 34.28049
## 45.00000 44.74278 34.62274
## 45.14286 42.48529 31.99303
## 45.28571 59.65997 48.80825
## 45.42857 50.18146 38.98181
## 45.57143 44.43231 32.89522
## 45.71429 45.07623 33.21130
## 
## $埼玉県$upper
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##                80%       95%
## 43.85714  65.92648  72.91127
## 44.00000  71.08463  78.35077
## 44.14286  68.20461  75.74161
## 44.28571 102.45043 110.24888
## 44.42857  83.63366  91.68508
## 44.57143  76.93165  85.22833
## 44.71429  76.27084  84.80574
## 44.85714  80.78855  90.52216
## 45.00000  82.97724  93.09728
## 45.14286  82.12601  92.61827
## 45.28571 100.65875 111.51047
## 45.42857  92.49473 103.69437
## 45.57143  88.02045  99.55753
## 45.71429  89.90300 101.76793
## 
## 
## $千葉県
## $千葉県$mean
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##  [1] 41.14387 39.92482 46.35993 41.79973 46.61510 42.25397 39.77295 42.39679
##  [9] 41.58521 45.06959 43.21139 45.49390 43.78254 42.47859
## 
## $千葉県$lower
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##               80%      95%
## 43.85714 29.73109 23.68953
## 44.00000 27.26382 20.56149
## 44.14286 33.33318 26.43723
## 44.28571 28.41721 21.33294
## 44.42857 32.88604 25.61831
## 44.57143 28.18690 20.74024
## 44.71429 25.37580 17.75441
## 44.85714 26.98205 18.82198
## 45.00000 25.59838 17.13547
## 45.14286 28.65539 19.96623
## 45.28571 26.38066 17.47101
## 45.42857 28.25670 19.13188
## 45.57143 26.14824 16.81320
## 45.71429 24.45594 14.91532
## 
## $千葉県$upper
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##               80%      95%
## 43.85714 52.55665 58.59821
## 44.00000 52.58582 59.28815
## 44.14286 59.38668 66.28263
## 44.28571 55.18224 62.26652
## 44.42857 60.34416 67.61188
## 44.57143 56.32104 63.76769
## 44.71429 54.17010 61.79149
## 44.85714 57.81153 65.97160
## 45.00000 57.57203 66.03494
## 45.14286 61.48379 70.17294
## 45.28571 60.04212 68.95177
## 45.42857 62.73110 71.85592
## 45.57143 61.41684 70.75188
## 45.71429 60.50125 70.04186
## 
## 
## $東京都
## $東京都$mean
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##  [1] 231.8803 197.0987 288.4446 259.0830 291.6828 191.9969 153.3508 236.1233
##  [9] 203.6080 294.7295 265.1511 297.5416 197.6536 158.8125
## 
## $東京都$lower
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##                80%       95%
## 43.85714 186.81680 162.96160
## 44.00000 145.64207 118.40259
## 44.14286 234.30569 205.64626
## 44.28571 202.55805 172.63555
## 44.42857 233.02090 201.96719
## 44.57143 131.41081  99.33849
## 44.71429  91.02449  58.03092
## 44.85714 167.01632 130.43328
## 45.00000 130.99030  92.54879
## 45.14286 219.65736 179.91658
## 45.28571 187.86110 146.94626
## 45.42857 218.23992 176.26017
## 45.57143 116.52158  73.57289
## 45.71429  76.01059  32.17792
## 
## $東京都$upper
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##               80%      95%
## 43.85714 276.9439 300.7991
## 44.00000 248.5553 275.7948
## 44.14286 342.5836 371.2430
## 44.28571 315.6079 345.5305
## 44.42857 350.3446 381.3983
## 44.57143 252.5829 284.6553
## 44.71429 215.6772 248.6708
## 44.85714 305.2303 341.8133
## 45.00000 276.2258 314.6673
## 45.14286 369.8016 409.5424
## 45.28571 342.4411 383.3559
## 45.42857 376.8432 418.8229
## 45.57143 278.7856 321.7343
## 45.71429 241.6143 285.4470
## 
## 
## $神奈川県
## $神奈川県$mean
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##  [1]  67.28248  57.57007  90.78147  85.74039 102.07531  77.31389  60.69181
##  [8]  71.76744  68.76805  82.89371  82.99430  89.28404  76.94157  71.45551
## 
## $神奈川県$lower
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##               80%      95%
## 43.85714 47.67956 37.30241
## 44.00000 35.82487 24.31365
## 44.14286 68.35792 56.48761
## 44.28571 62.97124 50.91798
## 44.42857 79.05898 66.87488
## 44.57143 54.08479 41.78805
## 44.71429 37.26345 24.86123
## 44.85714 45.68957 31.88478
## 45.00000 41.75320 27.45241
## 45.14286 55.35362 40.77477
## 45.28571 55.05669 40.26741
## 45.42857 60.99546 46.02039
## 45.57143 48.32138 33.17076
## 45.71429 42.51316 27.19201
## 
## $神奈川県$upper
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##                80%       95%
## 43.85714  86.88540  97.26255
## 44.00000  79.31528  90.82650
## 44.14286 113.20502 125.07532
## 44.28571 108.50954 120.56280
## 44.42857 125.09163 137.27574
## 44.57143 100.54300 112.83974
## 44.71429  84.12017  96.52239
## 44.85714  97.84531 111.65010
## 45.00000  95.78289 110.08368
## 45.14286 110.43380 125.01264
## 45.28571 110.93191 125.72119
## 45.42857 117.57262 132.54769
## 45.57143 105.56177 120.71238
## 45.71429 100.39787 115.71902
## 
## 
## $新潟県
## $新潟県$mean
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##  [1] 2.2790547 1.9650076 1.9926494 1.6552494 1.6199734 2.2732756 1.5443110
##  [8] 1.3429757 1.2210076 1.1218408 1.0412127 0.9756576 0.9223577 0.8790220
## 
## $新潟県$lower
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##                  80%        95%
## 43.85714  0.84342103  0.0834425
## 44.00000  0.48166559 -0.3035682
## 44.14286  0.47859458 -0.3228976
## 44.28571  0.12122912 -0.6908322
## 44.42857  0.07289625 -0.7460769
## 44.57143  0.71762718 -0.1058833
## 44.71429 -0.01697766 -0.8434739
## 44.85714 -0.25239867 -1.0969389
## 45.00000 -0.38093290 -1.2289490
## 45.14286 -0.48442555 -1.3347316
## 45.28571 -0.56790689 -1.4197233
## 45.42857 -0.63534537 -1.4881588
## 45.57143 -0.68988909 -1.5433610
## 45.71429 -0.73404656 -1.5879535
## 
## $新潟県$upper
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##               80%      95%
## 43.85714 3.714688 4.474667
## 44.00000 3.448350 4.233583
## 44.14286 3.506704 4.308197
## 44.28571 3.189270 4.001331
## 44.42857 3.167051 3.986024
## 44.57143 3.828924 4.652434
## 44.71429 3.105600 3.932096
## 44.85714 2.938350 3.782890
## 45.00000 2.822948 3.670964
## 45.14286 2.728107 3.578413
## 45.28571 2.650332 3.502149
## 45.42857 2.586661 3.439474
## 45.57143 2.534605 3.388076
## 45.71429 2.492090 3.345997
## 
## 
## $富山県
## $富山県$mean
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##  [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 
## $富山県$lower
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##                80%       95%
## 43.85714 -2.477680 -3.789285
## 44.00000 -2.617112 -4.002528
## 44.14286 -2.776887 -4.246883
## 44.28571 -2.899319 -4.434125
## 44.42857 -3.088861 -4.724006
## 44.57143 -3.201050 -4.895584
## 44.71429 -3.306181 -5.056367
## 44.85714 -3.395931 -5.193628
## 45.00000 -3.481814 -5.324975
## 45.14286 -3.553726 -5.434955
## 45.28571 -3.618477 -5.533983
## 45.42857 -3.675779 -5.621619
## 45.57143 -3.727489 -5.700702
## 45.71429 -3.773342 -5.770828
## 
## $富山県$upper
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##               80%      95%
## 43.85714 2.477680 3.789285
## 44.00000 2.617112 4.002528
## 44.14286 2.776887 4.246883
## 44.28571 2.899319 4.434125
## 44.42857 3.088861 4.724006
## 44.57143 3.201050 4.895584
## 44.71429 3.306181 5.056367
## 44.85714 3.395931 5.193628
## 45.00000 3.481814 5.324975
## 45.14286 3.553726 5.434955
## 45.28571 3.618477 5.533983
## 45.42857 3.675779 5.621619
## 45.57143 3.727489 5.700702
## 45.71429 3.773342 5.770828
## 
## 
## $石川県
## $石川県$mean
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##  [1] 1.796228 1.796228 1.796228 1.796228 1.796228 1.796228 1.796228 1.796228
##  [9] 1.796228 1.796228 1.796228 1.796228 1.796228 1.796228
## 
## $石川県$lower
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##                80%       95%
## 43.85714 -1.913833 -3.877820
## 44.00000 -2.122504 -4.196955
## 44.14286 -2.320612 -4.499935
## 44.28571 -2.509615 -4.788990
## 44.42857 -2.690663 -5.065880
## 44.57143 -2.864684 -5.332022
## 44.71429 -3.032438 -5.588579
## 44.85714 -3.194556 -5.836517
## 45.00000 -3.351571 -6.076651
## 45.14286 -3.503937 -6.309674
## 45.28571 -3.652043 -6.536183
## 45.42857 -3.796228 -6.756696
## 45.57143 -3.936789 -6.971664
## 45.71429 -4.073984 -7.181486
## 
## $石川県$upper
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##               80%       95%
## 43.85714 5.506288  7.470275
## 44.00000 5.714959  7.789411
## 44.14286 5.913067  8.092391
## 44.28571 6.102070  8.381445
## 44.42857 6.283118  8.658335
## 44.57143 6.457140  8.924477
## 44.71429 6.624893  9.181034
## 44.85714 6.787011  9.428972
## 45.00000 6.944026  9.669106
## 45.14286 7.096392  9.902129
## 45.28571 7.244498 10.128638
## 45.42857 7.388683 10.349151
## 45.57143 7.529244 10.564119
## 45.71429 7.666439 10.773941
## 
## 
## $福井県
## $福井県$mean
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##  [1] 1.698256 2.131467 1.957874 1.808517 1.680012 1.569448 1.474321 1.392475
##  [9] 1.322055 1.261468 1.209339 1.164488 1.125898 1.092697
## 
## $福井県$lower
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##                 80%        95%
## 43.85714 -0.1859223 -1.1833459
## 44.00000  0.1580163 -0.8866652
## 44.14286 -0.2546599 -1.4259044
## 44.28571 -0.5655430 -1.8222943
## 44.42857 -0.8068711 -2.1233474
## 44.57143 -0.9977619 -2.3567609
## 44.71429 -1.1507699 -2.5404090
## 44.85714 -1.2746540 -2.6865467
## 45.00000 -1.3757707 -2.8039135
## 45.14286 -1.4588594 -2.8989135
## 45.28571 -1.5275258 -2.9763343
## 45.42857 -1.5845548 -3.0398100
## 45.57143 -1.6321243 -3.0921333
## 45.71429 -1.6719549 -3.1354731
## 
## $福井県$upper
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##               80%      95%
## 43.85714 3.582434 4.579857
## 44.00000 4.104917 5.149599
## 44.14286 4.170407 5.341652
## 44.28571 4.182576 5.439327
## 44.42857 4.166895 5.483371
## 44.57143 4.136659 5.495658
## 44.71429 4.099412 5.489051
## 44.85714 4.059603 5.471496
## 45.00000 4.019881 5.448024
## 45.14286 3.981794 5.421849
## 45.28571 3.946203 5.395012
## 45.42857 3.913530 5.368785
## 45.57143 3.883921 5.343930
## 45.71429 3.857349 5.320867
## 
## 
## $山梨県
## $山梨県$mean
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##  [1] 1.868665 2.740244 2.740244 2.740244 2.740244 2.740244 2.740244 2.740244
##  [9] 2.740244 2.740244 2.740244 2.740244 2.740244 2.740244
## 
## $山梨県$lower
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##                80%         95%
## 43.85714 0.2931949 -0.54080869
## 44.00000 1.0416426  0.14245753
## 44.14286 1.0072798  0.08990416
## 44.28571 0.9735852  0.03837280
## 44.42857 0.9405214 -0.01219395
## 44.57143 0.9080542 -0.06184833
## 44.71429 0.8761523 -0.11063799
## 44.85714 0.8447873 -0.15860661
## 45.00000 0.8139330 -0.20579428
## 45.14286 0.7835651 -0.25223796
## 45.28571 0.7536614 -0.29797176
## 45.42857 0.7242011 -0.34302727
## 45.57143 0.6951653 -0.38743380
## 45.71429 0.6665359 -0.43121862
## 
## $山梨県$upper
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##               80%      95%
## 43.85714 3.444136 4.278139
## 44.00000 4.438845 5.338030
## 44.14286 4.473207 5.390583
## 44.28571 4.506902 5.442114
## 44.42857 4.539966 5.492681
## 44.57143 4.572433 5.542335
## 44.71429 4.604335 5.591125
## 44.85714 4.635700 5.639094
## 45.00000 4.666554 5.686281
## 45.14286 4.696922 5.732725
## 45.28571 4.726826 5.778459
## 45.42857 4.756286 5.823514
## 45.57143 4.785322 5.867921
## 45.71429 4.813951 5.911706
## 
## 
## $長野県
## $長野県$mean
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##  [1] 7.717558 8.637525 8.637525 8.637525 8.637525 8.637525 8.637525 8.637525
##  [9] 8.637525 8.637525 8.637525 8.637525 8.637525 8.637525
## 
## $長野県$lower
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##               80%      95%
## 43.85714 4.992415 3.549812
## 44.00000 5.825633 4.337107
## 44.14286 5.647148 4.064138
## 44.28571 5.478732 3.806568
## 44.42857 5.318852 3.562052
## 44.57143 5.166328 3.328787
## 44.71429 5.020229 3.105348
## 44.85714 4.879806 2.890590
## 45.00000 4.744446 2.683574
## 45.14286 4.613636 2.483517
## 45.28571 4.486946 2.289762
## 45.42857 4.364011 2.101749
## 45.57143 4.244514 1.918995
## 45.71429 4.128183 1.741082
## 
## $長野県$upper
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##               80%      95%
## 43.85714 10.44270 11.88530
## 44.00000 11.44942 12.93794
## 44.14286 11.62790 13.21091
## 44.28571 11.79632 13.46848
## 44.42857 11.95620 13.71300
## 44.57143 12.10872 13.94626
## 44.71429 12.25482 14.16970
## 44.85714 12.39524 14.38446
## 45.00000 12.53060 14.59148
## 45.14286 12.66141 14.79153
## 45.28571 12.78810 14.98529
## 45.42857 12.91104 15.17330
## 45.57143 13.03054 15.35605
## 45.71429 13.14687 15.53397
## 
## 
## $岐阜県
## $岐阜県$mean
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##  [1] 6.748964 5.989955 7.995318 9.421282 6.566775 7.135837 7.718835 7.602316
##  [9] 7.252924 8.010444 8.742645 7.227974 7.701042 7.696321
## 
## $岐阜県$lower
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##               80%         95%
## 43.85714 3.469159  1.73293441
## 44.00000 2.213782  0.21479662
## 44.14286 3.951809  1.81130390
## 44.28571 5.168628  2.91740893
## 44.42857 2.126136 -0.22459679
## 44.57143 2.518090  0.07360223
## 44.71429 2.931464  0.39718256
## 44.85714 2.395413 -0.36095515
## 45.00000 1.772428 -1.12877209
## 45.14286 2.298943 -0.72454364
## 45.28571 2.817408 -0.31922282
## 45.42857 1.098838 -2.14573051
## 45.57143 1.375273 -1.97338641
## 45.71429 1.180054 -2.26944926
## 
## $岐阜県$upper
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##                80%      95%
## 43.85714 10.028770 11.76499
## 44.00000  9.766128 11.76511
## 44.14286 12.038828 14.17933
## 44.28571 13.673936 15.92516
## 44.42857 11.007414 13.35815
## 44.57143 11.753584 14.19807
## 44.71429 12.506206 15.04049
## 44.85714 12.809219 15.56559
## 45.00000 12.733421 15.63462
## 45.14286 13.721946 16.74543
## 45.28571 14.667882 17.80451
## 45.42857 13.357109 16.60168
## 45.57143 14.026811 17.37547
## 45.71429 14.212588 17.66209
## 
## 
## $静岡県
## $静岡県$mean
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##  [1]  9.632446 13.647347 12.782793 14.258778 14.754742 12.991225 10.790326
##  [8] 13.341987 11.976252 12.943110 13.736726 15.686822 13.366149 12.558690
## 
## $静岡県$lower
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##               80%      95%
## 43.85714 5.780651 3.741634
## 44.00000 9.179549 6.814440
## 44.14286 8.007490 5.479597
## 44.28571 9.357926 6.763571
## 44.42857 9.600819 6.872498
## 44.57143 7.704207 4.905428
## 44.71429 5.286253 2.372572
## 44.85714 7.574677 4.521647
## 45.00000 5.942948 2.749110
## 45.14286 6.729375 3.440022
## 45.28571 7.322469 3.926966
## 45.42857 9.098868 5.611416
## 45.57143 6.591953 3.005909
## 45.71429 5.617276 1.942714
## 
## $静岡県$upper
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##               80%      95%
## 43.85714 13.48424 15.52326
## 44.00000 18.11514 20.48025
## 44.14286 17.55810 20.08599
## 44.28571 19.15963 21.75398
## 44.42857 19.90866 22.63699
## 44.57143 18.27824 21.07702
## 44.71429 16.29440 19.20808
## 44.85714 19.10930 22.16233
## 45.00000 18.00956 21.20339
## 45.14286 19.15685 22.44620
## 45.28571 20.15098 23.54649
## 45.42857 22.27478 25.76223
## 45.57143 20.14035 23.72639
## 45.71429 19.50010 23.17467
## 
## 
## $愛知県
## $愛知県$mean
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##  [1] 74.37024 70.84953 67.85149 79.59388 91.33363 70.11147 71.78722 69.31731
##  [9] 70.66695 74.56112 69.65826 83.89271 71.06541 68.48481
## 
## $愛知県$lower
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##               80%        95%
## 43.85714 60.63233  53.359911
## 44.00000 53.25469  43.940542
## 44.14286 46.19332  34.728178
## 44.28571 54.39928  41.062057
## 44.42857 63.83718  49.281438
## 44.57143 39.52185  23.328686
## 44.71429 39.01380  21.664591
## 44.85714 32.04317  12.311427
## 45.00000 29.28756   7.382638
## 45.14286 29.96609   6.358903
## 45.28571 21.44770  -4.073425
## 45.42857 32.71148   5.617777
## 45.57143 17.06749 -11.517280
## 45.71429 11.49469 -18.674051
## 
## $愛知県$upper
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##                80%       95%
## 43.85714  88.10816  95.38058
## 44.00000  88.44437  97.75851
## 44.14286  89.50966 100.97481
## 44.28571 104.78848 118.12570
## 44.42857 118.83007 133.38581
## 44.57143 100.70108 116.89425
## 44.71429 104.56065 121.90985
## 44.85714 106.59145 126.32319
## 45.00000 112.04634 133.95126
## 45.14286 119.15615 142.76333
## 45.28571 117.86882 143.38995
## 45.42857 135.07394 162.16765
## 45.57143 125.06333 153.64810
## 45.71429 125.47493 155.64367
## 
## 
## $三重県
## $三重県$mean
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##  [1] 3.343466 3.343466 3.343466 3.343466 3.343466 3.343466 3.343466 3.343466
##  [9] 3.343466 3.343466 3.343466 3.343466 3.343466 3.343466
## 
## $三重県$lower
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##                 80%       95%
## 43.85714 -0.3769195 -2.346373
## 44.00000 -0.5829000 -2.661393
## 44.14286 -0.7786004 -2.960691
## 44.28571 -0.9654217 -3.246409
## 44.42857 -1.1444729 -3.520244
## 44.57143 -1.3166496 -3.783566
## 44.71429 -1.4826876 -4.037499
## 44.85714 -1.6432003 -4.282982
## 45.00000 -1.7987050 -4.520806
## 45.14286 -1.9496431 -4.751646
## 45.28571 -2.0963948 -4.976083
## 45.42857 -2.2392903 -5.194623
## 45.57143 -2.3786184 -5.407707
## 45.71429 -2.5146336 -5.615724
## 
## $三重県$upper
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##               80%       95%
## 43.85714 7.063852  9.033305
## 44.00000 7.269832  9.348325
## 44.14286 7.465533  9.647623
## 44.28571 7.652354  9.933341
## 44.42857 7.831405 10.207176
## 44.57143 8.003582 10.470498
## 44.71429 8.169620 10.724431
## 44.85714 8.330133 10.969914
## 45.00000 8.485637 11.207738
## 45.14286 8.636575 11.438578
## 45.28571 8.783327 11.663015
## 45.42857 8.926223 11.881555
## 45.57143 9.065551 12.094639
## 45.71429 9.201566 12.302657
## 
## 
## $滋賀県
## $滋賀県$mean
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##  [1]  6.524112 11.888302  7.802086  9.488805  8.147681  8.675836  8.234666
##  [8]  8.399207  8.253719  8.304663  8.256553  8.272205  8.256249  8.261012
## 
## $滋賀県$lower
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##               80%         95%
## 43.85714 2.661404  0.61661017
## 44.00000 7.958182  5.87770286
## 44.14286 3.422905  1.10470760
## 44.28571 5.018439  2.65197036
## 44.42857 3.499513  1.03892077
## 44.57143 3.935129  1.42555074
## 44.71429 3.379540  0.80939151
## 44.85714 3.454896  0.83753609
## 45.00000 3.215105  0.54782433
## 45.14286 3.180441  0.46784242
## 45.28571 3.046201  0.28800722
## 45.42857 2.979336  0.17746086
## 45.57143 2.881576  0.03639543
## 45.71429 2.806474 -0.08098325
## 
## $滋賀県$upper
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##               80%      95%
## 43.85714 10.38682 12.43161
## 44.00000 15.81842 17.89890
## 44.14286 12.18127 14.49946
## 44.28571 13.95917 16.32564
## 44.42857 12.79585 15.25644
## 44.57143 13.41654 15.92612
## 44.71429 13.08979 15.65994
## 44.85714 13.34352 15.96088
## 45.00000 13.29233 15.95961
## 45.14286 13.42888 16.14148
## 45.28571 13.46691 16.22510
## 45.42857 13.56507 16.36695
## 45.57143 13.63092 16.47610
## 45.71429 13.71555 16.60301
## 
## 
## $京都府
## $京都府$mean
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##  [1] 12.54993 13.43231 13.68365 16.33572 16.46892 16.24087 13.19408 14.62564
##  [9] 13.91820 14.29303 16.02002 15.42784 15.65243 14.06199
## 
## $京都府$lower
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##               80%       95%
## 43.85714 6.202780 2.8427994
## 44.00000 6.485644 2.8083024
## 44.14286 6.509208 2.7112881
## 44.28571 9.007657 5.1284132
## 44.42857 9.007426 5.0575516
## 44.57143 8.653006 4.6362340
## 44.71429 5.483254 1.4013897
## 44.85714 6.549534 2.2743020
## 45.00000 5.637856 1.2545056
## 45.14286 5.847898 1.3773161
## 45.28571 7.422572 2.8713575
## 45.42857 6.683350 2.0542961
## 45.57143 6.764083 2.0588744
## 45.71429 5.032283 0.2522456
## 
## $京都府$upper
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##               80%      95%
## 43.85714 18.89709 22.25707
## 44.00000 20.37897 24.05631
## 44.14286 20.85809 24.65601
## 44.28571 23.66379 27.54303
## 44.42857 23.93041 27.88028
## 44.57143 23.82873 27.84550
## 44.71429 20.90490 24.98677
## 44.85714 22.70174 26.97698
## 45.00000 22.19855 26.58190
## 45.14286 22.73816 27.20874
## 45.28571 24.61747 29.16868
## 45.42857 24.17233 28.80139
## 45.57143 24.54079 29.24599
## 45.71429 23.09169 27.87173
## 
## 
## $大阪府
## $大阪府$mean
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##  [1] 163.4263 126.4003 146.2681 169.0622 180.3738 152.0676 111.3775 172.4432
##  [9] 135.6194 155.8475 177.4826 188.8171 162.6548 127.4922
## 
## $大阪府$lower
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##                80%       95%
## 43.85714 140.49822 128.36081
## 44.00000 101.50818  88.33107
## 44.14286 119.55594 105.41537
## 44.28571 140.64633 125.60389
## 44.42857 150.35076 134.45754
## 44.57143 120.51915 103.81843
## 44.71429  78.37408  60.90315
## 44.85714 134.03484 113.70267
## 45.00000  94.77352  73.15100
## 45.14286 112.70151  89.86142
## 45.28571 132.15312 108.15716
## 45.42857 141.40457 116.30591
## 45.57143 113.24701  87.09210
## 45.71429  76.16666  48.99654
## 
## $大阪府$upper
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##               80%      95%
## 43.85714 186.3544 198.4918
## 44.00000 151.2925 164.4696
## 44.14286 172.9803 187.1208
## 44.28571 197.4780 212.5205
## 44.42857 210.3968 226.2900
## 44.57143 183.6160 200.3167
## 44.71429 144.3808 161.8518
## 44.85714 210.8516 231.1838
## 45.00000 176.4653 198.0879
## 45.14286 198.9934 221.8335
## 45.28571 222.8120 246.8080
## 45.42857 236.2296 261.3282
## 45.57143 212.0626 238.2175
## 45.71429 178.8178 205.9879
## 
## 
## $兵庫県
## $兵庫県$mean
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##  [1] 45.46599 37.89292 41.19409 43.58257 34.64253 36.73899 32.07373 40.78359
##  [9] 37.73355 39.28337 39.98024 37.35790 37.97537 36.59822
## 
## $兵庫県$lower
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##               80%      95%
## 43.85714 36.55274 31.83434
## 44.00000 28.52431 23.56488
## 44.14286 31.39127 26.20197
## 44.28571 33.36397 27.95457
## 44.42857 24.02442 18.40353
## 44.57143 25.73586 19.91115
## 44.71429 20.69861 14.67698
## 44.85714 27.60048 20.62176
## 45.00000 23.92514 16.61542
## 45.14286 24.87679 17.25041
## 45.28571 24.99934 17.06894
## 45.42857 21.82391 13.60071
## 45.57143 21.90731 13.40140
## 45.71429 20.01329 11.23375
## 
## $兵庫県$upper
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##               80%      95%
## 43.85714 54.37925 59.09764
## 44.00000 47.26152 52.22095
## 44.14286 50.99691 56.18621
## 44.28571 53.80117 59.21057
## 44.42857 45.26064 50.88153
## 44.57143 47.74212 53.56682
## 44.71429 43.44886 49.47048
## 44.85714 53.96671 60.94543
## 45.00000 51.54196 58.85169
## 45.14286 53.68996 61.31634
## 45.28571 54.96113 62.89154
## 45.42857 52.89188 61.11508
## 45.57143 54.04342 62.54933
## 45.71429 53.18314 61.96268
## 
## 
## $奈良県
## $奈良県$mean
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##  [1] 9.670001 8.872383 9.331316 9.120938 9.248054 9.193252 9.228852 9.214858
##  [9] 9.224982 9.221525 9.224463 9.223658 9.224533 9.224367
## 
## $奈良県$lower
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##               80%       95%
## 43.85714 5.973213 4.0162520
## 44.00000 5.053606 3.0320683
## 44.14286 5.082187 2.8328336
## 44.28571 4.740362 2.4214257
## 44.42857 4.686356 2.2715392
## 44.57143 4.515913 2.0398789
## 44.71429 4.429596 1.8890231
## 44.85714 4.312331 1.7170907
## 45.00000 4.220273 1.5709402
## 45.14286 4.121301 1.4214053
## 45.28571 4.030663 1.2812321
## 45.42857 3.939403 1.1420868
## 45.57143 3.851564 1.0072857
## 45.71429 3.764612 0.8743926
## 
## $奈良県$upper
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##               80%      95%
## 43.85714 13.36679 15.32375
## 44.00000 12.69116 14.71270
## 44.14286 13.58044 15.82980
## 44.28571 13.50151 15.82045
## 44.42857 13.80975 16.22457
## 44.57143 13.87059 16.34662
## 44.71429 14.02811 16.56868
## 44.85714 14.11738 16.71263
## 45.00000 14.22969 16.87902
## 45.14286 14.32175 17.02164
## 45.28571 14.41826 17.16769
## 45.42857 14.50791 17.30523
## 45.57143 14.59750 17.44178
## 45.71429 14.68412 17.57434
## 
## 
## $和歌山県
## $和歌山県$mean
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##  [1] 1.8945013 1.7945111 1.6238488 1.9612712 0.9244145 1.6464220 1.7698384
##  [8] 1.6709498 1.6563416 1.6563416 1.6563416 1.6563416 1.6563416 1.6563416
## 
## $和歌山県$lower
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##                  80%        95%
## 43.85714  0.02618567 -0.9628409
## 44.00000 -0.20602580 -1.2650461
## 44.14286 -0.43835460 -1.5300191
## 44.28571 -0.16080749 -1.2841681
## 44.42857 -1.25589588 -2.4100825
## 44.57143 -0.59060463 -1.7748150
## 44.71429 -0.52250174 -1.7359933
## 44.85714 -0.62826472 -1.8453953
## 45.00000 -0.67356527 -1.9069435
## 45.14286 -0.71006728 -1.9627685
## 45.28571 -0.74601472 -2.0177454
## 45.42857 -0.78143214 -2.0719116
## 45.57143 -0.81634232 -2.1253021
## 45.71429 -0.85076643 -2.1779493
## 
## $和歌山県$upper
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##               80%      95%
## 43.85714 3.762817 4.751843
## 44.00000 3.795048 4.854068
## 44.14286 3.686052 4.777717
## 44.28571 4.083350 5.206711
## 44.42857 3.104725 4.258911
## 44.57143 3.883449 5.067659
## 44.71429 4.062179 5.275670
## 44.85714 3.970164 5.187295
## 45.00000 3.986249 5.219627
## 45.14286 4.022751 5.275452
## 45.28571 4.058698 5.330429
## 45.42857 4.094115 5.384595
## 45.57143 4.129026 5.437985
## 45.71429 4.163450 5.490633
## 
## 
## $鳥取県
## $鳥取県$mean
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##  [1] 0.3638222 0.1370523 0.3528799 0.1817402 0.3277734 0.2068996 0.3082044
##  [8] 0.2237355 0.2943154 0.2353921 0.2846015 0.2435106 0.2778245 0.2491706
## 
## $鳥取県$lower
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##                 80%        95%
## 43.85714 -0.4423061 -0.8690446
## 44.00000 -0.6903353 -1.1283279
## 44.14286 -0.4903811 -0.9367764
## 44.28571 -0.6615274 -1.1079263
## 44.42857 -0.5185287 -0.9665339
## 44.57143 -0.6395011 -1.0875585
## 44.71429 -0.5396795 -0.9885221
## 44.85714 -0.6241637 -1.0730145
## 45.00000 -0.5545331 -1.0038863
## 45.14286 -0.6134588 -1.0628134
## 45.28571 -0.5649199 -1.0146294
## 45.42857 -0.6060417 -1.0557675
## 45.57143 -0.5722350 -1.0222293
## 45.71429 -0.6009586 -1.0509898
## 
## $鳥取県$upper
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##                80%      95%
## 43.85714 1.1699505 1.596689
## 44.00000 0.9644399 1.402432
## 44.14286 1.1961409 1.642536
## 44.28571 1.0250079 1.471407
## 44.42857 1.1740755 1.622081
## 44.57143 1.0533002 1.501358
## 44.71429 1.1560884 1.604931
## 44.85714 1.0716348 1.520486
## 45.00000 1.1431638 1.592517
## 45.14286 1.0842430 1.533598
## 45.28571 1.1341229 1.583832
## 45.42857 1.0930628 1.542789
## 45.57143 1.1278840 1.577878
## 45.71429 1.0992998 1.549331
## 
## 
## $島根県
## $島根県$mean
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##  [1] 0.47 0.47 0.47 0.47 0.47 0.47 0.47 0.47 0.47 0.47 0.47 0.47 0.47 0.47
## 
## $島根県$lower
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##                80%       95%
## 43.85714 -6.368092 -9.987959
## 44.00000 -6.368092 -9.987959
## 44.14286 -6.368092 -9.987959
## 44.28571 -6.368092 -9.987959
## 44.42857 -6.368092 -9.987959
## 44.57143 -6.368092 -9.987959
## 44.71429 -6.368092 -9.987959
## 44.85714 -6.368092 -9.987959
## 45.00000 -6.368092 -9.987959
## 45.14286 -6.368092 -9.987959
## 45.28571 -6.368092 -9.987959
## 45.42857 -6.368092 -9.987959
## 45.57143 -6.368092 -9.987959
## 45.71429 -6.368092 -9.987959
## 
## $島根県$upper
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##               80%      95%
## 43.85714 7.308092 10.92796
## 44.00000 7.308092 10.92796
## 44.14286 7.308092 10.92796
## 44.28571 7.308092 10.92796
## 44.42857 7.308092 10.92796
## 44.57143 7.308092 10.92796
## 44.71429 7.308092 10.92796
## 44.85714 7.308092 10.92796
## 45.00000 7.308092 10.92796
## 45.14286 7.308092 10.92796
## 45.28571 7.308092 10.92796
## 45.42857 7.308092 10.92796
## 45.57143 7.308092 10.92796
## 45.71429 7.308092 10.92796
## 
## 
## $岡山県
## $岡山県$mean
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##  [1] 4.8274481 3.8298398 0.4406752 4.9497786 4.7669870 6.1515188 6.5186941
##  [8] 4.7422085 5.8109992 6.5605134 5.5506577 5.4834438 5.7769114 6.4798300
## 
## $岡山県$lower
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##                 80%         95%
## 43.85714  2.2700349  0.91622219
## 44.00000  0.9974246 -0.50196548
## 44.14286 -2.4983982 -4.05424959
## 44.28571  1.9408174  0.34796959
## 44.42857  1.6988720  0.07471002
## 44.57143  3.0281354  1.37471609
## 44.71429  3.3418680  1.66015784
## 44.85714  1.4236245 -0.33312780
## 45.00000  2.4066046  0.60442691
## 45.14286  3.0859173  1.24657729
## 45.28571  2.0112856  0.13765528
## 45.42857  1.8817157 -0.02492393
## 45.57143  2.1142849  0.17540756
## 45.71429  2.7574269  0.78690578
## 
## $岡山県$upper
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##                80%       95%
## 43.85714  7.384861  8.738674
## 44.00000  6.662255  8.161645
## 44.14286  3.379749  4.935600
## 44.28571  7.958740  9.551588
## 44.42857  7.835102  9.459264
## 44.57143  9.274902 10.928321
## 44.71429  9.695520 11.377230
## 44.85714  8.060793  9.817545
## 45.00000  9.215394 11.017571
## 45.14286 10.035109 11.874449
## 45.28571  9.090030 10.963660
## 45.42857  9.085172 10.991812
## 45.57143  9.439538 11.378415
## 45.71429 10.202233 12.172754
## 
## 
## $広島県
## $広島県$mean
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##  [1] 2.520641 2.649052 2.345470 2.341820 2.503705 2.471248 2.392233 2.426244
##  [9] 2.460951 2.435528 2.422510 2.438768 2.442222 2.432861
## 
## $広島県$lower
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##                80%       95%
## 43.85714 -1.018806 -2.892477
## 44.00000 -1.277333 -3.355835
## 44.14286 -1.772730 -3.952773
## 44.28571 -2.382829 -4.883907
## 44.42857 -2.764432 -5.553215
## 44.57143 -3.082345 -6.022240
## 44.71429 -3.468222 -6.570560
## 44.85714 -3.816597 -7.121358
## 45.00000 -4.104085 -7.579406
## 45.14286 -4.401261 -8.020438
## 45.28571 -4.700292 -8.470877
## 45.42857 -4.971616 -8.894436
## 45.57143 -5.229911 -9.291293
## 45.71429 -5.489302 -9.683042
## 
## $広島県$upper
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##                80%       95%
## 43.85714  6.060089  7.933759
## 44.00000  6.575436  8.653939
## 44.14286  6.463669  8.643712
## 44.28571  7.066469  9.567547
## 44.42857  7.771841 10.560625
## 44.57143  8.024842 10.964737
## 44.71429  8.252687 11.355025
## 44.85714  8.669086 11.973846
## 45.00000  9.025988 12.501309
## 45.14286  9.272317 12.891494
## 45.28571  9.545313 13.315897
## 45.42857  9.849152 13.771972
## 45.57143 10.114356 14.175738
## 45.71429 10.355024 14.548764
## 
## 
## $山口県
## $山口県$mean
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##  [1] 0.9651076 0.9651076 0.9651076 0.9651076 0.9651076 0.9651076 0.9651076
##  [8] 0.9651076 0.9651076 0.9651076 0.9651076 0.9651076 0.9651076 0.9651076
## 
## $山口県$lower
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##                 80%       95%
## 43.85714 -0.9245499 -1.924874
## 44.00000 -1.0003010 -2.040725
## 44.14286 -1.0732390 -2.152274
## 44.28571 -1.1436556 -2.259967
## 44.42857 -1.2117957 -2.364179
## 44.57143 -1.2778667 -2.465225
## 44.71429 -1.3420463 -2.563380
## 44.85714 -1.4044883 -2.658877
## 45.00000 -1.4653266 -2.751921
## 45.14286 -1.5246787 -2.842692
## 45.28571 -1.5826486 -2.931349
## 45.42857 -1.6393285 -3.018034
## 45.57143 -1.6948008 -3.102871
## 45.71429 -1.7491397 -3.185975
## 
## $山口県$upper
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##               80%      95%
## 43.85714 2.854765 3.855089
## 44.00000 2.930516 3.970941
## 44.14286 3.003454 4.082490
## 44.28571 3.073871 4.190183
## 44.42857 3.142011 4.294394
## 44.57143 3.208082 4.395441
## 44.71429 3.272261 4.493595
## 44.85714 3.334703 4.589092
## 45.00000 3.395542 4.682136
## 45.14286 3.454894 4.772907
## 45.28571 3.512864 4.861564
## 45.42857 3.569544 4.948249
## 45.57143 3.625016 5.033086
## 45.71429 3.679355 5.116191
## 
## 
## $徳島県
## $徳島県$mean
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##  [1] 0.8041317 0.5656579 0.5757955 1.1223200 0.1139642 0.4951366 0.6683621
##  [8] 0.5406530 0.5840154 0.5812010 0.1583691 0.7057025 0.6130418 0.4699541
## 
## $徳島県$lower
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##                 80%       95%
## 43.85714 -0.9330506 -1.852659
## 44.00000 -1.2546929 -2.218328
## 44.14286 -1.2634905 -2.237150
## 44.28571 -0.7357083 -1.719289
## 44.42857 -1.7626193 -2.756023
## 44.57143 -1.3998202 -2.402950
## 44.71429 -1.2447917 -2.257554
## 44.85714 -1.4708829 -2.535726
## 45.00000 -1.4622468 -2.545473
## 45.14286 -1.4895484 -2.585737
## 45.28571 -1.9365813 -3.045581
## 45.42857 -1.4131724 -2.534837
## 45.57143 -1.5294906 -2.663679
## 45.71429 -1.6959774 -2.842552
## 
## $徳島県$upper
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##               80%      95%
## 43.85714 2.541314 3.460923
## 44.00000 2.386009 3.349644
## 44.14286 2.415082 3.388741
## 44.28571 2.980348 3.963929
## 44.42857 1.990548 2.983951
## 44.57143 2.390093 3.393223
## 44.71429 2.581516 3.594278
## 44.85714 2.552189 3.617032
## 45.00000 2.630278 3.713503
## 45.14286 2.651950 3.748139
## 45.28571 2.253319 3.362319
## 45.42857 2.824577 3.946242
## 45.57143 2.755574 3.889762
## 45.71429 2.635886 3.782460
## 
## 
## $香川県
## $香川県$mean
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##  [1] 0.2124964 0.3868062 0.3868062 0.3868062 0.3868062 0.3868062 0.3868062
##  [8] 0.3868062 0.3868062 0.3868062 0.3868062 0.3868062 0.3868062 0.3868062
## 
## $香川県$lower
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##                80%       95%
## 43.85714 -1.150564 -1.872124
## 44.00000 -1.036470 -1.789907
## 44.14286 -1.040291 -1.795750
## 44.28571 -1.044101 -1.801577
## 44.42857 -1.047901 -1.807389
## 44.57143 -1.051691 -1.813185
## 44.71429 -1.055471 -1.818966
## 44.85714 -1.059241 -1.824732
## 45.00000 -1.063001 -1.830483
## 45.14286 -1.066752 -1.836219
## 45.28571 -1.070493 -1.841940
## 45.42857 -1.074224 -1.847647
## 45.57143 -1.077946 -1.853339
## 45.71429 -1.081659 -1.859017
## 
## $香川県$upper
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##               80%      95%
## 43.85714 1.575556 2.297117
## 44.00000 1.810083 2.563520
## 44.14286 1.813903 2.569362
## 44.28571 1.817713 2.575190
## 44.42857 1.821513 2.581001
## 44.57143 1.825303 2.586797
## 44.71429 1.829083 2.592579
## 44.85714 1.832853 2.598344
## 45.00000 1.836614 2.604095
## 45.14286 1.840364 2.609831
## 45.28571 1.844105 2.615553
## 45.42857 1.847837 2.621259
## 45.57143 1.851558 2.626952
## 45.71429 1.855271 2.632629
## 
## 
## $愛媛県
## $愛媛県$mean
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##  [1] 0.3227791 0.2498383 0.3967528 0.4005604 0.3985525 0.3000683 0.3924970
##  [8] 0.3435211 0.4143950 0.3577011 0.4133588 0.3629080 0.4100267 0.3665179
## 
## $愛媛県$lower
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##                80%       95%
## 43.85714 -1.310222 -2.174680
## 44.00000 -1.430061 -2.319346
## 44.14286 -1.310478 -2.214231
## 44.28571 -1.307313 -2.211406
## 44.42857 -1.313954 -2.220500
## 44.57143 -1.414679 -2.322411
## 44.71429 -1.324682 -2.233701
## 44.85714 -1.376694 -2.287321
## 45.00000 -1.310039 -2.222899
## 45.14286 -1.367111 -2.280171
## 45.28571 -1.312496 -2.226108
## 45.42857 -1.363566 -2.277506
## 45.57143 -1.317063 -2.231329
## 45.71429 -1.361071 -2.275602
## 
## $愛媛県$upper
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##               80%      95%
## 43.85714 1.955780 2.820238
## 44.00000 1.929738 2.819023
## 44.14286 2.103983 3.007737
## 44.28571 2.108434 3.012527
## 44.42857 2.111059 3.017605
## 44.57143 2.014816 2.922548
## 44.71429 2.109676 3.018695
## 44.85714 2.063736 2.974364
## 45.00000 2.138829 3.051689
## 45.14286 2.082513 2.995574
## 45.28571 2.139213 3.052826
## 45.42857 2.089382 3.003322
## 45.57143 2.137116 3.051382
## 45.71429 2.094107 3.008638
## 
## 
## $高知県
## $高知県$mean
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##  [1] 0.1636762 0.1859218 0.2064630 0.2254306 0.2429449 0.2591175 0.2740509
##  [8] 0.2878403 0.3005733 0.3123307 0.3231873 0.3332122 0.3424691 0.3510167
## 
## $高知県$lower
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##                80%       95%
## 43.85714 -1.547928 -2.453997
## 44.00000 -1.547525 -2.465156
## 44.14286 -1.545392 -2.472768
## 44.28571 -1.541969 -2.477574
## 44.42857 -1.537601 -2.480166
## 44.57143 -1.532562 -2.481020
## 44.71429 -1.527067 -2.480521
## 44.85714 -1.521286 -2.478980
## 45.00000 -1.515354 -2.476648
## 45.14286 -1.509375 -2.473728
## 45.28571 -1.503431 -2.470384
## 45.42857 -1.497584 -2.466749
## 45.57143 -1.491882 -2.462929
## 45.71429 -1.486360 -2.459009
## 
## $高知県$upper
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##               80%      95%
## 43.85714 1.875281 2.781349
## 44.00000 1.919368 2.837000
## 44.14286 1.958318 2.885694
## 44.28571 1.992830 2.928435
## 44.42857 2.023491 2.966056
## 44.57143 2.050797 2.999255
## 44.71429 2.075169 3.028623
## 44.85714 2.096967 3.054661
## 45.00000 2.116501 3.077794
## 45.14286 2.134036 3.098389
## 45.28571 2.149806 3.116759
## 45.42857 2.164009 3.133174
## 45.57143 2.176821 3.147868
## 45.71429 2.188394 3.161043
## 
## 
## $福岡県
## $福岡県$mean
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##  [1]  4.265612  7.593947  7.996901 10.810420  7.933814  5.352834  4.663094
##  [8]  4.878373  7.102804  7.995372  9.463465  7.606447  5.970012  5.241722
## 
## $福岡県$lower
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##                 80%       95%
## 43.85714  -9.312383 -16.50014
## 44.00000  -8.355075 -16.79798
## 44.14286  -9.244977 -18.37228
## 44.28571  -7.139064 -16.64095
## 44.42857 -10.628127 -20.45423
## 44.57143 -14.096347 -24.39212
## 44.71429 -16.126049 -27.13116
## 44.85714 -18.520671 -30.90738
## 45.00000 -18.143992 -31.50884
## 45.14286 -18.486175 -32.50466
## 45.28571 -17.822409 -32.26668
## 45.42857 -20.374269 -35.18637
## 45.57143 -22.873910 -38.14296
## 45.71429 -24.777216 -40.66828
## 
## $福岡県$upper
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##               80%      95%
## 43.85714 17.84361 25.03136
## 44.00000 23.54297 31.98587
## 44.14286 25.23878 34.36608
## 44.28571 28.75990 38.26178
## 44.42857 26.49576 36.32185
## 44.57143 24.80202 35.09779
## 44.71429 25.45224 36.45734
## 44.85714 28.27742 40.66412
## 45.00000 32.34960 45.71445
## 45.14286 34.47692 48.49540
## 45.28571 36.74934 51.19361
## 45.42857 35.58716 50.39926
## 45.57143 34.81393 50.08298
## 45.71429 35.26066 51.15173
## 
## 
## $佐賀県
## $佐賀県$mean
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##  [1] 1.287303 1.287303 1.287303 1.287303 1.287303 1.287303 1.287303 1.287303
##  [9] 1.287303 1.287303 1.287303 1.287303 1.287303 1.287303
## 
## $佐賀県$lower
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##                 80%       95%
## 43.85714 -0.6797452 -1.721038
## 44.00000 -0.7589846 -1.842224
## 44.14286 -0.8352679 -1.958889
## 44.28571 -0.9089032 -2.071504
## 44.42857 -0.9801484 -2.180465
## 44.57143 -1.0492223 -2.286104
## 44.71429 -1.1163119 -2.388709
## 44.85714 -1.1815791 -2.488526
## 45.00000 -1.2451648 -2.585772
## 45.14286 -1.3071927 -2.680636
## 45.28571 -1.3677718 -2.773283
## 45.42857 -1.4269992 -2.863864
## 45.57143 -1.4849615 -2.952510
## 45.71429 -1.5417366 -3.039340
## 
## $佐賀県$upper
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##               80%      95%
## 43.85714 3.254351 4.295643
## 44.00000 3.333590 4.416829
## 44.14286 3.409873 4.533494
## 44.28571 3.483509 4.646110
## 44.42857 3.554754 4.755070
## 44.57143 3.623828 4.860709
## 44.71429 3.690917 4.963314
## 44.85714 3.756185 5.063132
## 45.00000 3.819770 5.160378
## 45.14286 3.881798 5.255241
## 45.28571 3.942377 5.347889
## 45.42857 4.001605 5.438469
## 45.57143 4.059567 5.527115
## 45.71429 4.116342 5.613945
## 
## 
## $長崎県
## $長崎県$mean
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##  [1] -8.120624e-05 -1.125025e-04  1.938492e-01  5.391671e-01  4.535962e-02
##  [6] -8.494974e-05 -4.468070e-05 -9.785713e-05 -1.026053e-04  1.245580e-01
## [11]  3.665131e-01 -3.008461e-02 -1.110208e-04 -1.363308e-04
## 
## $長崎県$lower
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##                80%       95%
## 43.85714 -2.149746 -3.287709
## 44.00000 -2.392231 -3.658542
## 44.14286 -2.691034 -4.218198
## 44.28571 -2.635150 -4.315532
## 44.42857 -3.453581 -5.305808
## 44.57143 -3.767348 -5.761617
## 44.71429 -4.031348 -6.165391
## 44.85714 -4.424239 -6.766238
## 45.00000 -4.712949 -7.207778
## 45.14286 -4.894300 -7.551123
## 45.28571 -4.924072 -7.724739
## 45.42857 -5.587188 -8.528941
## 45.57143 -5.807553 -8.881827
## 45.71429 -6.049511 -9.251856
## 
## $長崎県$upper
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##               80%      95%
## 43.85714 2.149583 3.287547
## 44.00000 2.392006 3.658317
## 44.14286 3.078732 4.605897
## 44.28571 3.713484 5.393866
## 44.42857 3.544300 5.396527
## 44.57143 3.767178 5.761447
## 44.71429 4.031259 6.165301
## 44.85714 4.424043 6.766042
## 45.00000 4.712743 7.207573
## 45.14286 5.143416 7.800240
## 45.28571 5.657098 8.457765
## 45.42857 5.527019 8.468771
## 45.57143 5.807331 8.881605
## 45.71429 6.049238 9.251584
## 
## 
## $熊本県
## $熊本県$mean
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##  [1] -0.5703309  4.5560730  3.1670266  3.1670266  3.1670266  3.1670266
##  [7]  3.1670266  3.1670266  3.1670266  3.1670266  3.1670266  3.1670266
## [13]  3.1670266  3.1670266
## 
## $熊本県$lower
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##                80%        95%
## 43.85714 -4.810659  -7.055353
## 44.00000 -1.088027  -4.075833
## 44.14286 -2.633954  -5.704808
## 44.28571 -2.977735  -6.230575
## 44.42857 -3.303275  -6.728446
## 44.57143 -3.613204  -7.202441
## 44.71429 -3.909571  -7.655696
## 44.85714 -4.194016  -8.090717
## 45.00000 -4.467871  -8.509542
## 45.14286 -4.732238  -8.913855
## 45.28571 -4.988039  -9.305069
## 45.42857 -5.236056  -9.684380
## 45.57143 -5.476961 -10.052811
## 45.71429 -5.711331 -10.411249
## 
## $熊本県$upper
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##                80%       95%
## 43.85714  3.669997  5.914691
## 44.00000 10.200173 13.187979
## 44.14286  8.968008 12.038862
## 44.28571  9.311788 12.564628
## 44.42857  9.637329 13.062500
## 44.57143  9.947257 13.536494
## 44.71429 10.243624 13.989749
## 44.85714 10.528069 14.424770
## 45.00000 10.801924 14.843595
## 45.14286 11.066291 15.247909
## 45.28571 11.322092 15.639122
## 45.42857 11.570109 16.018433
## 45.57143 11.811014 16.386864
## 45.71429 12.045384 16.745302
## 
## 
## $大分県
## $大分県$mean
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##  [1] 0.1910348 0.2173869 0.2016613 0.3298859 0.3390420 0.3477154 0.3559315
##  [8] 0.3637145 0.3710871 0.3780710 0.3846867 0.3909537 0.3968902 0.4025137
## 
## $大分県$lower
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##                80%       95%
## 43.85714 -1.190157 -1.921316
## 44.00000 -1.374445 -2.217111
## 44.14286 -1.561533 -2.494912
## 44.28571 -1.497413 -2.464727
## 44.42857 -1.497834 -2.470217
## 44.57143 -1.497712 -2.474622
## 44.71429 -1.497136 -2.478090
## 44.85714 -1.496181 -2.480751
## 45.00000 -1.494915 -2.482717
## 45.14286 -1.493394 -2.484087
## 45.28571 -1.491666 -2.484948
## 45.42857 -1.489775 -2.485372
## 45.57143 -1.487756 -2.485427
## 45.71429 -1.485641 -2.485170
## 
## $大分県$upper
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##               80%      95%
## 43.85714 1.572227 2.303385
## 44.00000 1.809219 2.651884
## 44.14286 1.964856 2.898235
## 44.28571 2.157185 3.124499
## 44.42857 2.175918 3.148301
## 44.57143 2.193143 3.170053
## 44.71429 2.208999 3.189953
## 44.85714 2.223610 3.208180
## 45.00000 2.237089 3.224891
## 45.14286 2.249536 3.240229
## 45.28571 2.261040 3.254321
## 45.42857 2.271682 3.267280
## 45.57143 2.281536 3.279208
## 45.71429 2.290668 3.290197
## 
## 
## $宮崎県
## $宮崎県$mean
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##  [1] 0.3702083 0.2971183 0.2971183 0.2971183 0.2971183 0.2971183 0.2971183
##  [8] 0.2971183 0.2971183 0.2971183 0.2971183 0.2971183 0.2971183 0.2971183
## 
## $宮崎県$lower
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##                80%       95%
## 43.85714 -2.342518 -3.778549
## 44.00000 -2.530910 -4.027977
## 44.14286 -2.858394 -4.528821
## 44.28571 -3.154950 -4.982364
## 44.42857 -3.427971 -5.399914
## 44.57143 -3.682304 -5.788884
## 44.71429 -3.921332 -6.154444
## 44.85714 -4.147523 -6.500374
## 45.00000 -4.362748 -6.829532
## 45.14286 -4.568462 -7.144144
## 45.28571 -4.765824 -7.445984
## 45.42857 -4.955776 -7.736490
## 45.57143 -5.139095 -8.016852
## 45.71429 -5.316431 -8.288064
## 
## $宮崎県$upper
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##               80%      95%
## 43.85714 3.082935 4.518966
## 44.00000 3.125146 4.622214
## 44.14286 3.452631 5.123058
## 44.28571 3.749186 5.576601
## 44.42857 4.022208 5.994151
## 44.57143 4.276541 6.383120
## 44.71429 4.515568 6.748681
## 44.85714 4.741760 7.094611
## 45.00000 4.956984 7.423769
## 45.14286 5.162698 7.738381
## 45.28571 5.360061 8.040220
## 45.42857 5.550013 8.330727
## 45.57143 5.733332 8.611089
## 45.71429 5.910667 8.882300
## 
## 
## $鹿児島県
## $鹿児島県$mean
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##  [1] 1.849750 2.774684 3.051590 2.803905 3.384910 3.384910 3.384910 3.384910
##  [9] 3.384910 3.384910 3.384910 3.384910 3.384910 3.384910
## 
## $鹿児島県$lower
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##                80%       95%
## 43.85714 -2.151412 -4.269500
## 44.00000 -2.006249 -4.537122
## 44.14286 -1.986181 -4.653016
## 44.28571 -2.372391 -5.112557
## 44.42857 -1.852607 -4.625181
## 44.57143 -1.864980 -4.644104
## 44.71429 -1.877324 -4.662982
## 44.85714 -1.889639 -4.681816
## 45.00000 -1.901925 -4.700606
## 45.14286 -1.914183 -4.719353
## 45.28571 -1.926412 -4.738056
## 45.42857 -1.938613 -4.756716
## 45.57143 -1.950787 -4.775334
## 45.71429 -1.962933 -4.793909
## 
## $鹿児島県$upper
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##               80%       95%
## 43.85714 5.850912  7.968999
## 44.00000 7.555617 10.086490
## 44.14286 8.089362 10.756197
## 44.28571 7.980201 10.720366
## 44.42857 8.622427 11.395001
## 44.57143 8.634800 11.413923
## 44.71429 8.647143 11.432802
## 44.85714 8.659458 11.451636
## 45.00000 8.671744 11.470426
## 45.14286 8.684002 11.489172
## 45.28571 8.696232 11.507876
## 45.42857 8.708433 11.526536
## 45.57143 8.720606 11.545154
## 45.71429 8.732752 11.563729
## 
## 
## $沖縄県
## $沖縄県$mean
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##  [1] 23.36722 22.45760 24.85224 23.83396 26.13681 25.77844 23.67174 24.55832
##  [9] 24.55832 24.55832 24.55832 24.55832 24.55832 24.55832
## 
## $沖縄県$lower
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##                  80%         95%
## 43.85714 11.65082387   5.4485365
## 44.00000  9.65071892   2.8711635
## 44.14286 11.04070308   3.7293164
## 44.28571  9.08604941   1.2789771
## 44.42857 10.50853270   2.2354208
## 44.57143  9.31680501   0.6025418
## 44.71429  6.41694733  -2.7171857
## 44.85714  5.55886004  -4.4988477
## 45.00000  4.51017666  -6.1026706
## 45.14286  3.51368591  -7.6266717
## 45.28571  2.56229316  -9.0817013
## 45.42857  1.65037880 -10.4763540
## 45.57143  0.77340160 -11.8175749
## 45.71429 -0.07237054 -13.1110717
## 
## $沖縄県$upper
## Time Series:
## Start = c(43, 7) 
## End = c(45, 6) 
## Frequency = 7 
##               80%      95%
## 43.85714 35.08362 41.28591
## 44.00000 35.26449 42.04404
## 44.14286 38.66378 45.97517
## 44.28571 38.58187 46.38894
## 44.42857 41.76509 50.03821
## 44.57143 42.24008 50.95434
## 44.71429 40.92652 50.06066
## 44.85714 43.55778 53.61549
## 45.00000 44.60647 55.21932
## 45.14286 45.60296 56.74332
## 45.28571 46.55435 58.19835
## 45.42857 47.46627 59.59300
## 45.57143 48.34324 60.93422
## 45.71429 49.18902 62.22772